From 484e7dd106eab192e0c53250659d7ece1033694d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 25 Aug 2026 14:07:25 -0400 Subject: [PATCH 01/48] =?UTF-8?q?log=5Fviewer:=20readable=20dashboard=20?= =?UTF-8?q?=E2=80=94=20design=20tokens,=20filled=20chips,=20a=20legend?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The viewer worked but read as a wall of same-weight text: outline-only chips that all looked alike at a glance, a run table whose numbers did not line up, and a private chip notation (round tags, kind marks, rewards) documented only in tooltips. * design tokens replace the flat GitHub palette — one HSL scale defined on bare :root, with dark arriving either from the OS or from a new topbar toggle (auto / light / dark) that stamps data-theme on . Applied in so a stored choice never flashes the other theme first; * chips are filled (tinted background + saturated text) rather than outline-only: an episode strip is scanned, not read, and fills separate the states far faster than border colour alone. A run's kind vocabulary now also reaches the run page sidebar, where a learn query used to render as an unlabelled grey chip; * the run table gets a sticky header, uppercase micro-labels, row hover, and monospaced numerics so seeds, costs, timestamps and durations line up column-wise; * a "How to read a run row" legend spells out the notation on the index, open on a first visit and remembered closed thereafter. Column headers keep short labels and move provenance ("parsed from info.log") into tooltips, read once rather than on every row; * experiment groups open by default, so landing on the index shows the runs instead of a stack of headers hiding them. Presentation only: no change to what is parsed, served, or killed. Palette and density follow the kalshi-burner / trading1 dashboards. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .gitignore | 1 + scripts/log_viewer.py | 448 +++++++++++++++++++++++++++++++++--------- 2 files changed, 357 insertions(+), 92 deletions(-) diff --git a/.gitignore b/.gitignore index 291b12e16..7b8ba8dc1 100644 --- a/.gitignore +++ b/.gitignore @@ -41,3 +41,4 @@ predicators/datasets/vlm_input_data_prompts/vision_api/response.txt .idea/ paper/ +.venv/ diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 321b5762c..c6e665fb7 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1451,104 +1451,260 @@ def ansi_to_html(text: str) -> str: # ------------------------------------------------------------ HTML shell CSS = """ +/* ── Design tokens ────────────────────────────────────────────────── + Light is the default; dark arrives either from the OS (the media + query, skipped when the user has explicitly chosen light) or from + the topbar toggle, which stamps data-theme on . Every colour + is defined on bare :root so neither path can leave one undefined. */ :root { - --bg: #ffffff; --fg: #24292f; --muted: #57606a; --border: #d0d7de; - --panel: #f6f8fa; --accent: #0969da; --ok: #1a7f37; --bad: #cf222e; - --code-bg: #f6f8fa; --add: #dafbe1; --del: #ffebe9; + --bg: hsl(0, 0%, 100%); + --surface: hsl(240, 5%, 97%); + --surface2: hsl(240, 5%, 94%); + --surface3: hsl(240, 5%, 90%); + --border: hsl(240, 6%, 88%); + --border2: hsl(240, 5%, 80%); + --fg: hsl(222, 30%, 18%); + --bright: hsl(222, 47%, 8%); + --muted: hsl(220, 9%, 42%); + --muted2: hsl(220, 9%, 60%); + --accent: hsl(214, 90%, 38%); + --accent-dim: hsl(214, 100%, 94%); + --ok: hsl(142, 71%, 32%); + --ok-dim: hsl(142, 76%, 92%); + --bad: hsl(0, 78%, 46%); + --bad-dim: hsl(0, 84%, 95%); + --warn: hsl(35, 91%, 36%); + --warn-dim: hsl(35, 100%, 92%); + --violet: hsl(266, 60%, 46%); + --violet-dim: hsl(266, 90%, 96%); + --panel: var(--surface); + --code-bg: var(--surface2); + --add: hsl(142, 76%, 92%); + --del: hsl(0, 84%, 95%); + --shadow: 0 1px 2px hsl(0 0% 0% / .06); + --sans: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, + "Helvetica Neue", Arial, sans-serif; + --mono: "JetBrains Mono", "SF Mono", ui-monospace, SFMono-Regular, + Menlo, Consolas, monospace; + --topbar-h: 44px; } @media (prefers-color-scheme: dark) { - :root { - --bg: #0d1117; --fg: #c9d1d9; --muted: #8b949e; --border: #30363d; - --panel: #161b22; --accent: #58a6ff; --ok: #3fb950; --bad: #f85149; - --code-bg: #161b22; --add: #12261e; --del: #35181a; + :root:not([data-theme="light"]) { + --bg: hsl(220, 8%, 7%); + --surface: hsl(225, 27%, 11%); + --surface2: hsl(225, 27%, 14%); + --surface3: hsl(218, 30%, 18%); + --border: hsl(232, 22%, 18%); + --border2: hsl(232, 22%, 26%); + --fg: hsl(0, 0%, 82%); + --bright: hsl(0, 0%, 96%); + --muted: hsl(0, 0%, 58%); + --muted2: hsl(0, 0%, 40%); + --accent: hsl(207, 90%, 62%); + --accent-dim: hsl(213, 53%, 18%); + --ok: hsl(140, 70%, 50%); + --ok-dim: hsl(140, 50%, 12%); + --bad: hsl(348, 90%, 63%); + --bad-dim: hsl(348, 40%, 15%); + --warn: hsl(36, 100%, 60%); + --warn-dim: hsl(36, 40%, 13%); + --violet: hsl(266, 90%, 74%); + --violet-dim: hsl(266, 40%, 18%); + --add: hsl(140, 45%, 12%); + --del: hsl(348, 40%, 15%); + --shadow: 0 1px 2px hsl(0 0% 0% / .4); } } +:root[data-theme="dark"] { + --bg: hsl(220, 8%, 7%); + --surface: hsl(225, 27%, 11%); + --surface2: hsl(225, 27%, 14%); + --surface3: hsl(218, 30%, 18%); + --border: hsl(232, 22%, 18%); + --border2: hsl(232, 22%, 26%); + --fg: hsl(0, 0%, 82%); + --bright: hsl(0, 0%, 96%); + --muted: hsl(0, 0%, 58%); + --muted2: hsl(0, 0%, 40%); + --accent: hsl(207, 90%, 62%); + --accent-dim: hsl(213, 53%, 18%); + --ok: hsl(140, 70%, 50%); + --ok-dim: hsl(140, 50%, 12%); + --bad: hsl(348, 90%, 63%); + --bad-dim: hsl(348, 40%, 15%); + --warn: hsl(36, 100%, 60%); + --warn-dim: hsl(36, 40%, 13%); + --violet: hsl(266, 90%, 74%); + --violet-dim: hsl(266, 40%, 18%); + --add: hsl(140, 45%, 12%); + --del: hsl(348, 40%, 15%); + --shadow: 0 1px 2px hsl(0 0% 0% / .4); +} + * { box-sizing: border-box; } body { margin: 0; background: var(--bg); color: var(--fg); - font: 14px/1.5 -apple-system, BlinkMacSystemFont, "Segoe UI", Helvetica, - Arial, sans-serif; } + font: 13px/1.5 var(--sans); -webkit-font-smoothing: antialiased; } a { color: var(--accent); text-decoration: none; } a:hover { text-decoration: underline; } -code, pre { font: 12px/1.45 ui-monospace, SFMono-Regular, Menlo, monospace; } -.topbar { position: sticky; top: 0; z-index: 10; display: flex; gap: 12px; - align-items: center; padding: 8px 16px; background: var(--panel); - border-bottom: 1px solid var(--border); } -.topbar h1 { font-size: 15px; margin: 0; white-space: nowrap; } -.topbar input { flex: 1; max-width: 420px; padding: 4px 10px; - border: 1px solid var(--border); border-radius: 6px; - background: var(--bg); color: var(--fg); } -.topbar .crumb { color: var(--muted); font-size: 13px; overflow: hidden; - text-overflow: ellipsis; white-space: nowrap; } -button { padding: 4px 10px; border: 1px solid var(--border); - border-radius: 6px; background: var(--bg); color: var(--fg); - cursor: pointer; font-size: 12px; } -button:hover { border-color: var(--accent); } -.layout { display: flex; height: calc(100vh - 45px); } +code, pre { font: 12px/1.45 var(--mono); } +::-webkit-scrollbar { width: 9px; height: 9px; } +::-webkit-scrollbar-track { background: transparent; } +::-webkit-scrollbar-thumb { background: var(--border2); border-radius: 5px; } +::-webkit-scrollbar-thumb:hover { background: var(--muted2); } + +/* ── Topbar ─────────────────────────────────────────────────────── */ +.topbar { position: sticky; top: 0; z-index: 10; display: flex; gap: 8px; + align-items: center; height: var(--topbar-h); padding: 0 14px; + background: var(--surface); border-bottom: 1px solid var(--border); + user-select: none; } +.topbar h1 { font-size: 11px; margin: 0; white-space: nowrap; + font-weight: 700; letter-spacing: .12em; text-transform: uppercase; } +.topbar h1 a { color: var(--ok); } +.topbar h1 a:hover { text-decoration: none; opacity: .8; } +.topbar input { flex: 1; max-width: 340px; padding: 5px 10px; + border: 1px solid var(--border2); border-radius: 5px; + background: var(--bg); color: var(--bright); font-size: 12px; + font-family: var(--sans); } +.topbar input:focus { outline: none; border-color: var(--accent); + box-shadow: 0 0 0 2px var(--accent-dim); } +.topbar input::placeholder { color: var(--muted2); } +.topbar .crumb { color: var(--muted); font-size: 11px; overflow: hidden; + text-overflow: ellipsis; white-space: nowrap; font-family: var(--mono); + margin-left: auto; } +.topbar .sep { width: 1px; height: 16px; background: var(--border2); + flex-shrink: 0; } +button { height: 26px; padding: 0 10px; border: 1px solid transparent; + border-radius: 5px; background: transparent; color: var(--muted); + cursor: pointer; font-size: 11px; font-weight: 600; + font-family: var(--sans); letter-spacing: .02em; white-space: nowrap; + transition: color .1s, background .1s, border-color .1s; } +button:hover { background: var(--surface2); border-color: var(--border2); + color: var(--bright); } + +/* ── Layout ─────────────────────────────────────────────────────── */ +.layout { display: flex; height: calc(100vh - var(--topbar-h)); } .sidebar { width: 340px; min-width: 240px; overflow-y: auto; padding: 10px; - border-right: 1px solid var(--border); background: var(--panel); + border-right: 1px solid var(--border); background: var(--surface); resize: horizontal; } -.content { flex: 1; overflow-y: auto; padding: 16px 24px; } -.content { max-width: 100%; } +.content { flex: 1; overflow-y: auto; padding: 16px 22px; max-width: 100%; } .content pre.code { background: var(--code-bg); padding: 10px 12px; border: 1px solid var(--border); border-radius: 6px; overflow-x: auto; white-space: pre; } -.content h2, .content h3 { border-bottom: 1px solid var(--border); - padding-bottom: 4px; } +.content h2, .content h3 { color: var(--bright); + border-bottom: 1px solid var(--border); padding-bottom: 5px; } + +/* ── Collapsible sections ───────────────────────────────────────── */ details.section, details.turn { border: 1px solid var(--border); border-radius: 6px; margin: 8px 0; background: var(--bg); } details.section > summary, details.turn > summary { cursor: pointer; - padding: 6px 12px; font-weight: 600; background: var(--panel); - border-radius: 6px; } + padding: 6px 12px; font-weight: 600; color: var(--bright); + background: var(--surface); border-radius: 6px; } +details.section > summary:hover, details.turn > summary:hover { + background: var(--surface2); } details[open].section > summary, details[open].turn > summary { - border-bottom: 1px solid var(--border); - border-radius: 6px 6px 0 0; } + border-bottom: 1px solid var(--border); border-radius: 6px 6px 0 0; } details.section > *:not(summary), details.turn > *:not(summary) { margin-left: 12px; margin-right: 12px; } details.turn { margin-left: 8px; } -summary .hint { color: var(--muted); font-weight: 400; font-size: 12px; } -.chip { display: inline-block; padding: 0 7px; border-radius: 10px; - font-size: 11px; line-height: 18px; border: 1px solid var(--border); - color: var(--muted); margin-right: 3px; white-space: nowrap; } -.chip.ok { color: var(--ok); border-color: var(--ok); } -.chip.bad { color: var(--bad); border-color: var(--bad); } -.chip.kind-explore { color: #b083f0; border-color: #b083f0; } -.chip.kind-learn { color: #daaa3f; border-color: #daaa3f; } +summary .hint { color: var(--muted); font-weight: 400; font-size: 11px; } + +/* ── Chips ────────────────────────────────────────────────────────── + Filled (tinted background + saturated text) rather than outline-only: + a run's episode strip is read at a glance, and fills separate the + states far faster than border colour alone. */ +.chip { display: inline-block; padding: 1px 7px; border-radius: 4px; + font: 600 10px/16px var(--mono); border: 1px solid var(--border2); + background: var(--surface2); color: var(--muted); margin-right: 3px; + white-space: nowrap; vertical-align: middle; } +.chip.ok { color: var(--ok); border-color: var(--ok); background: var(--ok-dim); } +.chip.bad { color: var(--bad); border-color: var(--bad); background: var(--bad-dim); } +.chip.kind-explore { color: var(--violet); border-color: var(--violet); + background: var(--violet-dim); } +.chip.kind-learn { color: var(--warn); border-color: var(--warn); + background: var(--warn-dim); } +.chip.kind-test { color: var(--accent); border-color: var(--accent); + background: var(--accent-dim); } /* Lifecycle, not verdict: green and red stay reserved for env evals. */ .chip.live { color: var(--accent); border-color: var(--accent); + background: var(--accent-dim); } +.chip.live::before { content: "\\25cf"; margin-right: 4px; animation: pulse 1.8s ease-in-out infinite; } -.chip.stopped { color: #daaa3f; border-color: #daaa3f; } -@keyframes pulse { 50% { opacity: .45; } } +.chip.done { color: var(--muted); border-color: var(--border2); } +.chip.stopped { color: var(--warn); border-color: var(--warn); + background: var(--warn-dim); } +@keyframes pulse { 50% { opacity: .3; } } @media (prefers-reduced-motion: reduce) { - .chip.live { animation: none; } + .chip.live::before { animation: none; } } -.banner { padding: 10px 14px; border-radius: 6px; margin-bottom: 12px; - border: 1px solid var(--border); background: var(--panel); - display: flex; gap: 18px; flex-wrap: wrap; } -.banner.warn { border-color: var(--bad); color: var(--bad); - font-weight: 600; } + +/* ── Banner / stat strip ────────────────────────────────────────── */ +.banner { padding: 9px 14px; border-radius: 6px; margin-bottom: 12px; + border: 1px solid var(--border); background: var(--surface); + display: flex; gap: 20px; flex-wrap: wrap; align-items: center; + font-size: 12px; } +.banner.warn { border-color: var(--bad); background: var(--bad-dim); + color: var(--bad); font-weight: 600; } +.banner b, .banner .num { font-family: var(--mono); color: var(--bright); } .ok { color: var(--ok); font-weight: 700; } .bad { color: var(--bad); font-weight: 700; } -table.grid { border-collapse: collapse; margin: 10px 0; } -table.grid th, table.grid td { border: 1px solid var(--border); - padding: 4px 10px; text-align: left; font-size: 13px; } -table.grid th { background: var(--panel); } + +/* ── Legend ───────────────────────────────────────────────────────── + The episode strip is a dense private notation (round tags, kind + chips, marks, rewards). Spelling it out on the page beats making + every reader hover each chip for its tooltip. */ +details.legend { border: 1px solid var(--border); border-radius: 6px; + background: var(--surface); margin: 0 0 12px; } +details.legend > summary { cursor: pointer; padding: 7px 12px; + font-size: 11px; font-weight: 700; letter-spacing: .08em; + text-transform: uppercase; color: var(--muted); } +details.legend > summary:hover { color: var(--bright); } +details.legend[open] > summary { border-bottom: 1px solid var(--border); } +.legend-grid { display: grid; gap: 6px 14px; padding: 10px 12px; + grid-template-columns: max-content 1fr; align-items: baseline; + font-size: 12px; } +.legend-grid dt { text-align: right; } +.legend-grid dd { margin: 0; color: var(--muted); } +.legend-grid dd b { color: var(--bright); font-weight: 600; } + +/* ── Tables ─────────────────────────────────────────────────────── */ +table.grid { border-collapse: collapse; margin: 0; } +table.grid th, table.grid td { border-bottom: 1px solid var(--border); + padding: 5px 10px; text-align: left; font-size: 12px; + vertical-align: middle; } +table.grid th { background: var(--surface2); color: var(--muted); + font-size: 9px; font-weight: 700; letter-spacing: .1em; + text-transform: uppercase; position: sticky; top: 0; z-index: 2; + border-bottom: 1px solid var(--border2); } table.grid.runs { table-layout: fixed; } -table.epgrid { border-collapse: collapse; margin: 0; - table-layout: fixed; } +tr.runrow:hover > td { background: var(--surface2); } +tr.runrow td:nth-child(2) a { color: var(--bright); font-weight: 600; } +tr.runrow td:nth-child(2) a:hover { color: var(--accent); } +/* Numbers, timestamps and seeds line up column-wise when monospaced. */ +tr.runrow td:nth-child(3), tr.runrow td:nth-last-child(-n+4) { + font-family: var(--mono); font-size: 11px; } +table.epgrid { border-collapse: collapse; margin: 0; table-layout: fixed; } table.epgrid td { border: none; padding: 1px 0; line-height: 20px; vertical-align: top; } +table.epgrid td.rnd { color: var(--muted2); font-family: var(--mono); + font-size: 10px; } + +/* ── Sidebar nav ────────────────────────────────────────────────── */ .sidebar .nav a { display: block; padding: 3px 6px; border-radius: 4px; - color: var(--fg); font-size: 13px; overflow: hidden; + color: var(--fg); font-size: 12px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } -.sidebar .nav a:hover { background: var(--bg); text-decoration: none; } +.sidebar .nav a:hover { background: var(--surface2); + text-decoration: none; } .sidebar .nav a.active { background: var(--accent); color: #fff; } .sidebar .nav a.active .chip, .sidebar .nav a.active .muted { - color: #fff; border-color: #fff; } -.sidebar h3 { font-size: 12px; text-transform: uppercase; - letter-spacing: .5px; color: var(--muted); margin: 14px 0 4px; } + color: #fff; border-color: #fff; background: transparent; } +.sidebar h3 { font-size: 9px; text-transform: uppercase; + letter-spacing: .14em; color: var(--muted); margin: 16px 0 5px; + font-weight: 700; } .sidebar details { margin-left: 8px; } -.sidebar details summary { cursor: pointer; font-size: 13px; +.sidebar details summary { cursor: pointer; font-size: 12px; color: var(--muted); } + +/* ── Media ──────────────────────────────────────────────────────── */ .thumbs { display: flex; flex-wrap: wrap; gap: 6px; margin: 6px 0; } /* width/height attrs on reserve layout space before lazy images load (needed for exact scroll restore); auto on the free axis keeps @@ -1566,47 +1722,63 @@ def ansi_to_html(text: str) -> str: figure.vid video { max-width: 480px; width: 100%; border: 1px solid var(--border); border-radius: 6px; display: block; background: #000; } -figure.vid figcaption { font-size: 11px; margin-top: 4px; } +figure.vid figcaption { font-size: 11px; margin-top: 4px; + color: var(--muted); font-family: var(--mono); } .gallery { display: grid; gap: 8px; grid-template-columns: repeat(auto-fill, minmax(180px, 1fr)); } .gallery a { text-align: center; font-size: 11px; color: var(--muted); overflow-wrap: anywhere; } .gallery img { width: 100%; height: auto; border: 1px solid var(--border); border-radius: 4px; } + +/* ── Logs and diffs ─────────────────────────────────────────────── */ pre.logview { white-space: pre-wrap; overflow-wrap: anywhere; } .diff .add { background: var(--add); display: block; } .diff .del { background: var(--del); display: block; } .diff .hunk { color: var(--accent); display: block; } + +/* ── Overlays ───────────────────────────────────────────────────── */ #lightbox { position: fixed; inset: 0; background: rgba(0,0,0,.85); display: none; align-items: center; justify-content: center; z-index: 100; cursor: zoom-out; } #lightbox img { max-width: 96vw; max-height: 96vh; } .runrow.hidden { display: none; } #refreshpill { position: fixed; right: 18px; bottom: 18px; z-index: 50; - background: var(--accent); color: #fff; border: none; + background: var(--accent); color: #fff; border: none; height: auto; padding: 8px 14px; border-radius: 18px; font-weight: 600; box-shadow: 0 2px 8px rgba(0,0,0,.35); } +#refreshpill:hover { background: var(--accent); color: #fff; + border-color: transparent; opacity: .9; } + +/* ── Run groups ─────────────────────────────────────────────────── */ details.grp { border: 1px solid var(--border); border-radius: 6px; - margin: 8px 0; } -details.grp > summary { cursor: pointer; padding: 6px 12px; - font-weight: 600; background: var(--panel); border-radius: 6px; } -details.grp[open] > summary { border-bottom: 1px solid var(--border); - border-radius: 6px 6px 0 0; } -details.grp.family > summary { font-size: 15px; } -details.grp > *:not(summary) { margin: 8px 12px; } + margin: 8px 0; background: var(--bg); overflow: hidden; } +details.grp > summary { cursor: pointer; padding: 7px 12px; + font-weight: 600; color: var(--bright); background: var(--surface); } +details.grp > summary:hover { background: var(--surface2); } +details.grp[open] > summary { border-bottom: 1px solid var(--border); } +details.grp.family { border-color: var(--border2); } +details.grp.family > summary { font-size: 13px; letter-spacing: .01em; } +details.grp.exp > summary { font-size: 12px; } +details.grp.exp { margin: 8px 12px; } +details.grp > *:not(summary) { margin: 0; } +details.grp.family > details.grp { margin: 8px 12px; } details.grp.hidden { display: none; } -.muted { color: var(--muted); } -button.copybtn { padding: 0 3px; margin-left: 5px; border: none; - background: none; color: var(--muted); font-size: 12px; +.muted { color: var(--muted); font-weight: 400; } + +/* ── Row action buttons ─────────────────────────────────────────── */ +button.copybtn { height: auto; padding: 0 3px; margin-left: 5px; + border: none; background: none; color: var(--muted2); font-size: 12px; visibility: hidden; } tr.runrow:hover button.copybtn { visibility: visible; } -button.copybtn:hover { color: var(--accent); } +button.copybtn:hover { color: var(--accent); background: none; } button.copybtn.copied { color: var(--ok); visibility: visible; } -button.rowbtn { padding: 0 5px; margin-left: 5px; +button.rowbtn { height: auto; padding: 0 5px; margin-left: 5px; border: 1px solid transparent; border-radius: 4px; background: none; - color: var(--muted); font-size: 11px; visibility: hidden; } + color: var(--muted2); font-size: 11px; visibility: hidden; } tr.runrow:hover button.rowbtn { visibility: visible; } -button.rowbtn:hover { color: var(--bad); border-color: var(--bad); } +button.rowbtn:hover { color: var(--bad); border-color: var(--bad); + background: var(--bad-dim); } """ + f""" table.epgrid td.task {{ width: {TASK_W}px; }} table.epgrid td.misc {{ width: {MISC_W}px; }} @@ -1617,6 +1789,28 @@ def ansi_to_html(text: str) -> str: function $(s, r) { return (r || document).querySelector(s); } function $all(s, r) { return Array.from((r || document).querySelectorAll(s)); } +// Theme: 'auto' (follow the OS) | 'light' | 'dark'. Applied here in +// , before the body paints, so a stored choice never flashes the +// other theme first. +function themeMode() { return localStorage.getItem('lv-theme') || 'auto'; } +function applyTheme() { + var m = themeMode(); + if (m === 'auto') document.documentElement.removeAttribute('data-theme'); + else document.documentElement.setAttribute('data-theme', m); +} +function toggleTheme() { + var next = {auto: 'light', light: 'dark', dark: 'auto'}[themeMode()]; + localStorage.setItem('lv-theme', next); + applyTheme(); + paintThemeBtn(); +} +function paintThemeBtn() { + var b = $('#themebtn'); + if (b) b.textContent = {auto: '\\u25d1 auto', light: '\\u2600 light', + dark: '\\u263e dark'}[themeMode()]; +} +applyTheme(); + // Lightbox for images. document.addEventListener('click', function(e) { var a = e.target.closest('a.thumb, a.shot'); @@ -1679,7 +1873,19 @@ def ansi_to_html(text: str) -> str: if (v !== null) d.open = v === '1'; }); } +// The legend ships open so a first visit explains itself; someone who +// already knows the notation closes it once and it stays closed. +function restoreLegend() { + var l = $('details.legend'); + if (!l) return; + var v = localStorage.getItem('lv-legend'); + if (v !== null) l.open = v === '1'; + l.addEventListener('toggle', function() { + localStorage.setItem('lv-legend', l.open ? '1' : '0'); + }); +} document.addEventListener('DOMContentLoaded', restoreGroups); +document.addEventListener('DOMContentLoaded', restoreLegend); document.addEventListener('toggle', function(e) { var d = e.target; if (d.classList && d.classList.contains('grp') && !window._filtering) @@ -1892,6 +2098,7 @@ def ansi_to_html(text: str) -> str: } document.addEventListener('DOMContentLoaded', function() { paintAutoBtn(); + paintThemeBtn(); paintSortBtn(); applySort(); pollStamp(); @@ -1926,13 +2133,52 @@ def ansi_to_html(text: str) -> str: def page(title: str, topbar_extra: str, body: str) -> str: """Wrap body in the full HTML page shell (topbar, CSS, and JS).""" - return ( - "" - f"{esc(title)}" - f"" - "

log viewer

" - f"{topbar_extra}" - f"
{body}") + return ("" + f"{esc(title)}" + f"" + "

log viewer

" + f"{topbar_extra}" + "" + "" + f"
{body}") + + +# The episode strip packs a run's whole history into one cell using a +# private notation (round tags, kind chips, marks, rewards). Every chip +# carries a tooltip, but hovering a dozen of them to learn the alphabet +# is not reading - so the alphabet is written down, open on a first +# visit and collapsible for readers who already know it. +LEGEND_HTML = ( + "
How to read a run row" + "
" + "
r1
" + "
One round of the online loop: r1 is the pre-learning " + "phase, r2 the work after learning cycle 0, and so on.
" + "
001 explore \u2713 0.90
" + "
Exploration episode on a train task: query number, then " + "the env verdict \u2713 accepted / \u2717 rejected, then its " + "reward.
" + "
003 learn
" + "
Learning query: the agent synthesizes predicates, " + "simulator code, and physical params. No env verdict to show.
" + "
004 t0 \u2713 0.80 " + "005 t1 \u2717
" + "
Test episode on held-out task tN, green " + "solved and red failed. This is the column that measures the " + "method; explore episodes do not.
" + "
running " + "done " + "interrupted
" + "
Run lifecycle, not verdict: done means info.log " + "ends with main.py's completion line, interrupted means it " + "does not and no process is alive.
" + "
test results
" + "
Per round, solved/total and the mean env reward - the " + "headline number for the run.
" + "
cost
" + "
Total USD the run's agent queries reported.
" + "
") def chip(label: Any, cls: str = "", title: str = "") -> str: @@ -2193,16 +2439,25 @@ def index_page() -> str: if ts >= newest.get(key, (0.0, ""))[0]: newest[key] = (ts, r["rel"]) body = [ - "
" + "
", LEGEND_HTML ] if not runs: body.append( f"

No run_* directories found under {esc(LOGS_ROOT)}.

") - table_head = ("runseedstatus" - "episodestest results (info.log)" - "coststartedmodified" - "time") + # Provenance and units live in the header tooltips rather than in + # the labels: the labels are read on every row, the provenance once. + table_head = ( + "runseed" + "status" + "" + "episodes" + "test results" + "" + "coststartedmodified" + "time" + "") for fam in sorted(families): exps = families[fam] fam_runs = [r for rs in exps.values() for r in rs] @@ -2221,8 +2476,12 @@ def index_page() -> str: run_row(r, summaries[r["rel"]], layout, live, newest[( r["exp"], r["seed"])][1] == r["rel"]) for r in exps[expname]) + # Open by default: a first visit should land on the run + # table itself, not on a stack of headers hiding it. A + # reader who collapses one gets that remembered instead + # (restoreGroups reapplies localStorage on load). body.append(f"
" + f"{esc(expname)}' open>" f"{esc(expname)} " f"({len(exps[expname])} runs)" f" Optional[str]: mark, cls, title = test_mark(ep) elif "env_accepted" in ep: mark, cls, title = explore_mark(ep) + # An episode with no env verdict (a learn query) still + # gets its kind colour, so the sidebar speaks the same chip + # vocabulary as the index's episode strip. + if not cls: + cls = "kind-" + ep["kind"] cost = (f" ${ep['solve_cost']:.2f}" if "solve_cost" in ep else "") task_str = (f" task{ep['task']}" if ep["task"] is not None else "") mark_str = " " + mark if mark else "" @@ -2805,7 +3069,7 @@ def stat_row(label: str, fn: Callable[[Dict[str, Any]], str]) -> str: "total cost", lambda s: (f"${s['total_cost']:.2f}") if s["total_cost"] else "-")) rows_html = "".join(rows) - body = ("

Run comparison

" "
" f"{head}{rows_html}
") From a2e200ae8de4c41e97cec0067f69a23a10d07045 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 25 Aug 2026 14:12:50 -0400 Subject: [PATCH 02/48] =?UTF-8?q?log=5Fviewer:=20a=20replay=20reel=20?= =?UTF-8?q?=E2=80=94=20watch=20a=20run's=20episodes=20in=20order?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The run page pairs each video with its agent transcript, which is right for reading one episode and wrong for the question this viewer gets asked most: did the robot get better? Answering it meant finding the videos dir by hand and sorting filenames. /replays?d= lays every recorded episode out oldest-first, each clip captioned in plain language — which round, whether it was a practice episode (data gathering) or a held-out test (the score), and the env verdict with its reward. Reachable from a ▶ on each index row and from the run page's banner. Deliberately not chips: this page exists for readers who do not know the pipeline's shorthand, so the same facts are spelled out in words. A replanned task's two transcripts share one video, so identical players are shown once rather than reading as two attempts. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/log_viewer.py | 107 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index c6e665fb7..e0d659285 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1568,6 +1568,7 @@ def ansi_to_html(text: str) -> str: .topbar input:focus { outline: none; border-color: var(--accent); box-shadow: 0 0 0 2px var(--accent-dim); } .topbar input::placeholder { color: var(--muted2); } +.topbar a { white-space: nowrap; font-size: 11px; font-weight: 600; } .topbar .crumb { color: var(--muted); font-size: 11px; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; font-family: var(--mono); margin-left: auto; } @@ -1750,6 +1751,24 @@ def ansi_to_html(text: str) -> str: #refreshpill:hover { background: var(--accent); color: #fff; border-color: transparent; opacity: .9; } +/* ── Replay reel ────────────────────────────────────────────────── */ +section.replay { border: 1px solid var(--border); border-radius: 8px; + background: var(--surface); padding: 14px 16px; margin: 0 0 14px; + max-width: 720px; } +section.replay h3 { margin: 0 0 4px; font-size: 14px; color: var(--bright); + border: none; padding: 0; } +section.replay p { margin: 0 0 10px; font-size: 12px; } +.replay-meta { font-size: 12px; margin-bottom: 6px; font-family: var(--mono); } +section.replay figure.vid video { max-width: 100%; } +a.playlink { margin-left: 6px; color: var(--muted2); font-size: 11px; } +tr.runrow:hover a.playlink { color: var(--accent); } +a.playlink:hover { text-decoration: none; } +a.watchlink { display: inline-block; height: 26px; line-height: 24px; + padding: 0 10px; border: 1px solid var(--accent); border-radius: 5px; + background: var(--accent-dim); color: var(--accent); font-size: 11px; + font-weight: 600; } +a.watchlink:hover { text-decoration: none; filter: brightness(1.1); } + /* ── Run groups ─────────────────────────────────────────────────── */ details.grp { border: 1px solid var(--border); border-radius: 6px; margin: 8px 0; background: var(--bg); overflow: hidden; } @@ -2401,6 +2420,9 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], layout: Dict[str, Any], f"" "" f"{esc(r['name'])}" + f"▶" f"{del_btn}" f"{esc(r['seed'])}" @@ -2546,6 +2568,83 @@ def file_tree_html(run_abs: str, run_rel: str, rel: str = "") -> str: return "".join(out) +def replay_caption(ep: Dict[str, Any]) -> Tuple[str, str, str]: + """(heading, plain-English explanation, verdict html) for one clip. + + The chips elsewhere are shorthand for people who already know the + pipeline. Here the same facts are spelled out, because this page + exists for the question "what is the robot actually doing?" rather + than "how did this run score". + """ + rnd = int(ep.get("round", 0)) + if ep["kind"] == "test": + head = f"Round {rnd + 1} - test on task {ep['task']}" + why = ("A held-out task the robot did not practise on. This is " + "the episode that scores the run.") + else: + head = f"Round {rnd + 1} - practice episode" + why = ("The robot tries the task itself to gather data. What it " + "sees here is what the next round learns from.") + verdict = "no env verdict recorded" + solved = ep.get("env_solved", ep.get("env_accepted")) + if solved is not None: + reward = (f" · reward {ep['env_reward']:.2f}" + if "env_reward" in ep else "") + cls, word = ("ok", "solved") if solved else ("bad", "failed") + verdict = f"{word}{reward}" + return head, why, verdict + + +def replays_page(run_rel: str) -> Optional[str]: + """Every recorded episode of a run, in order, as a scrollable reel. + + The run page pairs each video with its agent transcript, which is + the right thing when reading one episode and the wrong thing when + the question is whether behaviour improved over the run. Here the + clips are simply laid out in time order with the round, the intent, + and the verdict beside each. + """ + run_abs = safe_join(run_rel) + if not run_abs or not os.path.isdir(run_abs): + return None + summary = run_summary(run_rel) + assert summary is not None + cards = [] + seen: Set[str] = set() + for ep in summary["episodes"]: + vids = episode_videos(ep, run_rel) + if not vids: + continue + # A replanned task's two transcripts share one video; showing it + # twice would read as two attempts that never happened. + if vids in seen: + continue + seen.add(vids) + head, why, verdict = replay_caption(ep) + cards.append(f"

{esc(head)}

" + f"
{verdict}
" + f"

{why}

{vids}" + f"read this episode's " + "transcript →
") + if not cards: + cards = [ + "

This run recorded no videos. They are " + "written when main.py runs with --make_test_videos / " + "--make_interaction_videos.

" + ] + intro = ("") + body = (f"
{intro}" + f"{''.join(cards)}
") + topbar = (f"← run detail" + f"{esc(run_rel)}") + return page(run_rel + " replays - log viewer", topbar, body) + + def run_page(run_rel: str) -> Optional[str]: """Single-run page: episode sidebar, file tree, and content pane.""" run_abs = safe_join(run_rel) @@ -2611,6 +2710,8 @@ def run_page(run_rel: str) -> Optional[str]: "overflow:hidden'>" f"" @@ -3208,6 +3309,12 @@ def route(self) -> None: self.send_html("

Run not found.

", status=404) else: self.send_html(html_text) + elif route == "/replays": + html_text = replays_page(params.get("d", "")) + if html_text is None: + self.send_html("

Run not found.

", status=404) + else: + self.send_html(html_text) elif route == "/view": self.send_html( view_fragment(params.get("d", ""), params.get("f", ""))) From ed9239c9c1ecdb4e05798dbfcfca9a5768df28c1 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 25 Aug 2026 14:26:41 -0400 Subject: [PATCH 03/48] log_viewer: flatten the index, and make the reel the pipeline view MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two rounds of feedback on the same page said the same thing: the hierarchy was chrome, not information. The index is one flat table now. Approach and experiment were collapsible group headings nesting every row two deep; they are columns, visible on every row and typeable into the filter box, which is what the groups were actually used for. The episode strip is gone from it entirely - a dense private notation is the wrong thing to meet a reader with, and the run page and the reel both carry that detail already. What is left is what a run IS (approach, experiment, seed) and how it went (status, result, cost, when). The replay reel becomes the run's pipeline, not a list of clips: episodes grouped into the rounds of the online loop, clips small and side by side so a round is taken in at a glance, and between rounds the sysID verdict - the physical parameters the fit recovered from the practice episodes and handed to the planner. Watching top to bottom is watching belief and behaviour move together. A ▶ watch button opens it from every index row. Also: the reading key explains the columns on screen rather than a chip alphabet the index no longer uses, and says plainly that cost is the SDK's token valuation, not a subscription bill. Players seek to #t=0.1 so a wall of clips shows first frames rather than black rectangles. Removes the episode-grid layout machinery (grid_layout, grid_width, episode_grid, _split_episodes, the chip builders, and the geometry constants) with the grid that used them. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/log_viewer.py | 522 ++++++++++++++++-------------------------- 1 file changed, 201 insertions(+), 321 deletions(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index e0d659285..6d7bdf21d 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -124,6 +124,13 @@ # main.py logs this only after the pipeline returns, so it is the one # trustworthy "this run completed" marker; a crash or a kill leaves none. DONE_RE = re.compile(r"^Main script terminated in") +# The sysID verdict of a learning cycle: the physical parameters the fit +# recovered and handed to the planner. This is what "it learned +# something" looks like in a log, so the replay reel shows it between +# the rounds it separates. +APPLIED_PARAMS_RE = re.compile( + r"Applied identified physical params to base env: \{(.*)\}") +PARAM_KV_RE = re.compile(r"'([\w]+)':\s*'?(-?[\d.eE+]+)'?") # A live run is a main.py process whose flags name the run's log dir, # which utils.configure_logging builds as approach/experiment_id/seed. PS_ARG_RE = re.compile(r"--(approach|experiment_id|seed)[= ]+(\S+)") @@ -135,17 +142,9 @@ ".sh", ".tex" } CODE_EXTS = {".py"} -# Episode-grid geometry, in px. A task always gets TASK_W of space no -# matter how many times it was retried, so its chips land at the same x in -# every run and experiment; retries stack inside that space. MISC_W holds -# the round's explore/learn chips, which are unbounded and so wrap too. -TASK_W = 108 -ROUND_W = 34 -MISC_W = 3 * TASK_W # Fixed widths for the remaining columns of the index runs table, in the # order they are declared there. None is the episodes column, whose width # depends on the task count and is filled in at render time. -RUN_COL_W = (30, 200, 62, 96, None, 178, 68, 132, 132, 72) # Episodes of one run, in file order; see _parse_episode for the fields. EpList = List[Dict[str, Any]] # A run's videos as {(task, cycle tag): [(filename, is_failure)]}. @@ -845,6 +844,7 @@ def _parse_info_log(path: str) -> Dict[str, Any]: test_round: Dict[int, int] = {} pending: List[int] = [] pending_test: List[int] = [] + fits: List[Dict[str, Any]] = [] last_explore: Optional[int] = None done = False for line in text.splitlines(): @@ -884,6 +884,23 @@ def _parse_info_log(path: str) -> Dict[str, Any]: if verdict is not None: verdict["reward"] = float(tm.group(2)) continue + m = APPLIED_PARAMS_RE.search(line) + if m: + params = {k: float(v) for k, v in PARAM_KV_RE.findall(m.group(1))} + # Several fits can land in one cycle (the agent refits as it + # edits its simulator); the last one is what the planner + # actually carries into the next round. + if params: + if fits and fits[-1]["after_round"] == len(rounds): + fits[-1]["params"] = params + fits[-1]["refits"] += 1 + else: + fits.append({ + "after_round": len(rounds), + "params": params, + "refits": 1, + }) + continue m = SAVED_EP_RE.search(line) if m: if m.group(2) == "explore": @@ -918,6 +935,7 @@ def _parse_info_log(path: str) -> Dict[str, Any]: "rounds": rounds, "explore": explore, "test_round": test_round, + "fits": fits, "done": done, "video_rel": m_vid.group(1) if m_vid else "", } @@ -1011,6 +1029,7 @@ def run_summary(run_rel: str) -> Optional[Dict[str, Any]]: "test_results": parsed.get("totals", []), "explore_results": _explore_results(episodes), "rounds": rounds, + "fits": parsed.get("fits", []), "total_cost": total_cost, "done": parsed.get("done", False), } @@ -1683,12 +1702,6 @@ def ansi_to_html(text: str) -> str: /* Numbers, timestamps and seeds line up column-wise when monospaced. */ tr.runrow td:nth-child(3), tr.runrow td:nth-last-child(-n+4) { font-family: var(--mono); font-size: 11px; } -table.epgrid { border-collapse: collapse; margin: 0; table-layout: fixed; } -table.epgrid td { border: none; padding: 1px 0; line-height: 20px; - vertical-align: top; } -table.epgrid td.rnd { color: var(--muted2); font-family: var(--mono); - font-size: 10px; } - /* ── Sidebar nav ────────────────────────────────────────────────── */ .sidebar .nav a { display: block; padding: 3px 6px; border-radius: 4px; color: var(--fg); font-size: 12px; overflow: hidden; @@ -1752,14 +1765,26 @@ def ansi_to_html(text: str) -> str: border-color: transparent; opacity: .9; } /* ── Replay reel ────────────────────────────────────────────────── */ -section.replay { border: 1px solid var(--border); border-radius: 8px; - background: var(--surface); padding: 14px 16px; margin: 0 0 14px; - max-width: 720px; } -section.replay h3 { margin: 0 0 4px; font-size: 14px; color: var(--bright); - border: none; padding: 0; } -section.replay p { margin: 0 0 10px; font-size: 12px; } -.replay-meta { font-size: 12px; margin-bottom: 6px; font-family: var(--mono); } -section.replay figure.vid video { max-width: 100%; } +section.round { border: 1px solid var(--border); border-radius: 8px; + background: var(--surface); padding: 12px 14px; margin: 0 0 14px; } +section.round h3 { margin: 0 0 10px; font-size: 12px; color: var(--muted); + border: none; padding: 0; text-transform: uppercase; + letter-spacing: .14em; font-weight: 700; } +/* Clips sit side by side and small: the point of this page is + comparing episodes across a run, which a column of full-width + players makes impossible without scrolling past each one. */ +.strip { display: flex; flex-wrap: wrap; gap: 12px; } +figure.clip { margin: 0; width: 260px; } +figure.clip .vids { margin: 0; } +figure.clip figure.vid video { width: 260px; max-width: 260px; } +figure.clip figure.vid figcaption { display: none; } +figure.clip > figcaption { font-size: 11px; margin-top: 5px; + line-height: 1.45; } +.fitband { margin-top: 12px; padding: 10px 12px; border-radius: 6px; + border: 1px solid var(--ok); background: var(--ok-dim); font-size: 12px; } +.fitvals { display: flex; flex-wrap: wrap; gap: 6px 18px; margin: 6px 0; } +.fitvals code { color: var(--muted); } +.fitvals b { font-family: var(--mono); color: var(--bright); } a.playlink { margin-left: 6px; color: var(--muted2); font-size: 11px; } tr.runrow:hover a.playlink { color: var(--accent); } a.playlink:hover { text-decoration: none; } @@ -1769,21 +1794,12 @@ def ansi_to_html(text: str) -> str: font-weight: 600; } a.watchlink:hover { text-decoration: none; filter: brightness(1.1); } -/* ── Run groups ─────────────────────────────────────────────────── */ -details.grp { border: 1px solid var(--border); border-radius: 6px; - margin: 8px 0; background: var(--bg); overflow: hidden; } -details.grp > summary { cursor: pointer; padding: 7px 12px; - font-weight: 600; color: var(--bright); background: var(--surface); } -details.grp > summary:hover { background: var(--surface2); } -details.grp[open] > summary { border-bottom: 1px solid var(--border); } -details.grp.family { border-color: var(--border2); } -details.grp.family > summary { font-size: 13px; letter-spacing: .01em; } -details.grp.exp > summary { font-size: 12px; } -details.grp.exp { margin: 8px 12px; } -details.grp > *:not(summary) { margin: 0; } -details.grp.family > details.grp { margin: 8px 12px; } -details.grp.hidden { display: none; } .muted { color: var(--muted); font-weight: 400; } +.help { display: inline-block; width: 14px; height: 14px; margin-left: 6px; + border: 1px solid var(--border2); border-radius: 50%; color: var(--muted2); + font: 700 9px/12px var(--sans); text-align: center; cursor: help; + vertical-align: middle; } +.help:hover { color: var(--accent); border-color: var(--accent); } /* ── Row action buttons ─────────────────────────────────────────── */ button.copybtn { height: auto; padding: 0 3px; margin-left: 5px; @@ -1798,10 +1814,6 @@ def ansi_to_html(text: str) -> str: tr.runrow:hover button.rowbtn { visibility: visible; } button.rowbtn:hover { color: var(--bad); border-color: var(--bad); background: var(--bad-dim); } -""" + f""" -table.epgrid td.task {{ width: {TASK_W}px; }} -table.epgrid td.misc {{ width: {MISC_W}px; }} -table.epgrid td.rnd {{ width: {ROUND_W}px; }} """ JS = """ @@ -1885,63 +1897,13 @@ def ansi_to_html(text: str) -> str: } // Index page: filter + compare + collapsible groups. -function groupKey(d) { return 'lv-grp:' + d.dataset.key; } -function restoreGroups() { - $all('details.grp').forEach(function(d) { - var v = localStorage.getItem(groupKey(d)); - if (v !== null) d.open = v === '1'; - }); -} -// The legend ships open so a first visit explains itself; someone who -// already knows the notation closes it once and it stays closed. -function restoreLegend() { - var l = $('details.legend'); - if (!l) return; - var v = localStorage.getItem('lv-legend'); - if (v !== null) l.open = v === '1'; - l.addEventListener('toggle', function() { - localStorage.setItem('lv-legend', l.open ? '1' : '0'); - }); -} -document.addEventListener('DOMContentLoaded', restoreGroups); -document.addEventListener('DOMContentLoaded', restoreLegend); -document.addEventListener('toggle', function(e) { - var d = e.target; - if (d.classList && d.classList.contains('grp') && !window._filtering) - localStorage.setItem(groupKey(d), d.open ? '1' : '0'); -}, true); -function setAllGroups(open) { - window._filtering = true; - $all('details.grp').forEach(function(d) { - d.open = open; - localStorage.setItem(groupKey(d), open ? '1' : '0'); - }); - window._filtering = false; -} function filterRuns(text) { text = text.toLowerCase(); sessionStorage.setItem('lv-filter', text); - window._filtering = true; $all('.runrow').forEach(function(row) { row.classList.toggle('hidden', !!text && row.dataset.key.indexOf(text) === -1); }); - $all('details.grp.exp').forEach(function(d) { - var any = $all('.runrow', d).some(function(r) { - return !r.classList.contains('hidden'); - }); - d.classList.toggle('hidden', !any); - if (text) d.open = any; - }); - $all('details.grp.family').forEach(function(d) { - var any = $all('details.grp.exp', d).some(function(x) { - return !x.classList.contains('hidden'); - }); - d.classList.toggle('hidden', !any); - if (text) d.open = any; - }); - if (!text) restoreGroups(); - window._filtering = false; } // Index page: sort runs by seed (the server order) or by start time. // Time mode interleaves each experiment's seeds newest-first; the @@ -2163,40 +2125,32 @@ def page(title: str, topbar_extra: str, body: str) -> str: f"
{body}") -# The episode strip packs a run's whole history into one cell using a -# private notation (round tags, kind chips, marks, rewards). Every chip -# carries a tooltip, but hovering a dozen of them to learn the alphabet -# is not reading - so the alphabet is written down, open on a first -# visit and collapsible for readers who already know it. +# A short reading key for the table below it. It used to explain the +# index's chip notation; the flat table has none, so it explains the +# columns that are actually on screen instead. LEGEND_HTML = ( - "
How to read a run row" - "
" - "
r1
" - "
One round of the online loop: r1 is the pre-learning " - "phase, r2 the work after learning cycle 0, and so on.
" - "
001 explore \u2713 0.90
" - "
Exploration episode on a train task: query number, then " - "the env verdict \u2713 accepted / \u2717 rejected, then its " - "reward.
" - "
003 learn
" - "
Learning query: the agent synthesizes predicates, " - "simulator code, and physical params. No env verdict to show.
" - "
004 t0 \u2713 0.80 " - "005 t1 \u2717
" - "
Test episode on held-out task tN, green " - "solved and red failed. This is the column that measures the " - "method; explore episodes do not.
" - "
running " - "done " - "interrupted
" - "
Run lifecycle, not verdict: done means info.log " - "ends with main.py's completion line, interrupted means it " - "does not and no process is alive.
" - "
test results
" - "
Per round, solved/total and the mean env reward - the " - "headline number for the run.
" + "
What am I looking at?" + "
" + "
run
" + "
One execution of main.py, named by when it " + "started. Click it for the transcripts and files; click " + "▶ watch for the videos.
" + "
approach
" + "
The method. An oracle_* approach is handed the " + "ground-truth model and learns nothing - it is an upper bound. An " + "agent_* one has to learn.
" + "
experiment
" + "
One environment + arm configuration. Change a flag and it is " + "a different experiment; rerun with another seed and it is not." + "
" + "
result
" + "
solved/total on held-out tasks, with the mean reward. " + "This is the score. On the domino tasks reward is 1 for a " + "solve minus a penalty per domino spent, so 0.90 beats 0.80.
" "
cost
" - "
Total USD the run's agent queries reported.
" + "
What the agent SDK reported those tokens would cost at API " + "rates. On a Claude subscription nothing is billed per query - " + "read it as a usage meter, not a bill.
" "
") @@ -2246,83 +2200,6 @@ def explore_mark(ep: Dict[str, Any]) -> Tuple[str, str, str]: return mark, cls, title -def _split_episodes( - episodes: EpList -) -> Tuple[Dict[int, Dict[int, EpList]], Dict[int, EpList]]: - """Episodes bucketed by round, as (test-by-task, non-test).""" - tests: Dict[int, Dict[int, EpList]] = {} - misc: Dict[int, EpList] = {} - for ep in episodes: - rnd = ep.get("round", 0) - if ep["kind"] == "test" and ep["task"] is not None: - tests.setdefault(rnd, {}).setdefault(ep["task"], []).append(ep) - else: - misc.setdefault(rnd, []).append(ep) - return tests, misc - - -def grid_layout(runs: List[EpList]) -> Dict[str, Any]: - """Column layout shared by every run's episode grid. - - Holding the task set and the round column fixed across every run on - the page is what lets task t1's chips land at the same x whether the - run above it retried t0 twice or not, and whether it belongs to the - same experiment or not. - """ - tasks: Set[int] = set() - rounds = False - misc = False - for episodes in runs: - by_round, misc_by_round = _split_episodes(episodes) - rounds = rounds or max(list(by_round) + list(misc_by_round), - default=0) > 0 - misc = misc or bool(misc_by_round) - for by_task in by_round.values(): - tasks.update(by_task) - return {"tasks": sorted(tasks), "rounds": rounds, "misc": misc} - - -def grid_width(layout: Dict[str, Any]) -> int: - """Pixel width of an episode grid drawn with this layout.""" - return (len(layout["tasks"]) * TASK_W + - (ROUND_W if layout["rounds"] else 0) + - (MISC_W if layout["misc"] else 0)) - - -def episode_grid(episodes: EpList, - layout: Optional[Dict[str, Any]] = None) -> str: - """Chips laid out one row per test round, one column per task. - - Vertical alignment makes it easy to compare a task's outcome against - earlier rounds and against other runs; retries within a round stack - inside their task's column rather than widening it. - """ - if layout is None: - layout = grid_layout([episodes]) - tests, misc = _split_episodes(episodes) - if not tests and not misc: - return "" - n_rounds = max(list(tests) + list(misc)) + 1 - rows = [] - for rnd in range(n_rounds): - cells = [] - if layout["rounds"]: - label = f"r{int(rnd + 1)}" if n_rounds > 1 else "" - cells.append(f"{label}") - for task in layout["tasks"]: - # One retry per line: two short chips would otherwise share a - # line while longer ones stack, making the column read ragged. - chips = "".join(f"
{_test_chip(ep)}
" - for ep in tests.get(rnd, {}).get(task, [])) - cells.append(f"{chips}") - if layout["misc"]: - chips = "".join(_misc_chip(ep) for ep in misc.get(rnd, [])) - cells.append(f"{chips}") - rows.append(f"{''.join(cells)}") - return (f"" - f"{''.join(rows)}
") - - def run_status(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, is_newest: bool) -> Tuple[str, str]: """(label, css class) for a run's lifecycle state. @@ -2361,44 +2238,22 @@ def status_chip(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, return chip(label, cls, title) -def _test_chip(ep: Dict[str, Any]) -> str: - """Chip for one test episode, e.g. "003 t1 ✓".""" - mark, cls, title = test_mark(ep) - label = f"{int(ep['num']):03} t{ep['task']}" - if mark: - label += " " + mark - return chip(label, cls, title) - - -def _misc_chip(ep: Dict[str, Any]) -> str: - """Chip for one non-test episode, e.g. "002 explore ✓ 0.70".""" - label = f"{int(ep['num']):03} {ep['kind']}" - title = "" - if "env_accepted" in ep: - mark, _, title = explore_mark(ep) - label += " " + mark - return chip(label, "kind-" + ep["kind"], title) - - -# ----------------------------------------------------------------- pages - - -def run_row(r: Dict[str, Any], summary: Dict[str, Any], layout: Dict[str, Any], - live: LiveProcs, is_newest: bool) -> str: - """Table row summarizing one run for the index page.""" - eps = summary.get("episodes", []) +def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, + is_newest: bool) -> str: + """One flat row on the index: what it was, how it went, where to look.""" tr_str = test_results_str(summary) or "-" cost = summary.get("total_cost", 0.0) fmt = "%Y-%m-%d %H:%M" start_ts = _run_start_ts(r["name"], r["mtime"]) sstr = datetime.datetime.fromtimestamp(start_ts).strftime(fmt) - mstr = datetime.datetime.fromtimestamp(r["activity"]).strftime(fmt) dur_str = _fmt_duration(max(0.0, r["activity"] - start_ts)) status, _ = run_status(r, summary, live, is_newest) - # The status joins the filter key, so "running" narrows to live runs. + fam, _, expname = r["exp"].partition("/") + expname = expname or fam + # Approach and experiment join the filter key, so what used to be a + # group heading is now something you type into the filter box. key = f"{r['exp']} {r['seed']} {r['name']} {status}".lower() cost_str = f"${cost:.2f}" if cost else "-" - # Path to paste into a terminal at the server's working directory. copy_path = os.path.relpath(os.path.join(LOGS_ROOT, r["rel"])) is_live = status == "running" esc_rel = esc(r["rel"]) @@ -2413,43 +2268,39 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], layout: Dict[str, Any], live_flag = "true" if is_live else "false" del_btn = (f"") + "\u2715") return ("" f"" "" + f"" + "▶ watch" f"{esc(r['name'])}" - f"▶" f"{del_btn}" + f"title='Copy run path'>\u29c9{del_btn}" + f"{esc(fam)}" + f"{esc(expname)}" f"{esc(r['seed'])}" f"{status_chip(r, summary, live, is_newest)}{kill_btn}" - f"{episode_grid(eps, layout)}" - f"{esc(tr_str)}" + f"{esc(tr_str)}" f"{cost_str}" f"{sstr}" - f"{mstr}" f"{dur_str}") def index_page() -> str: - """Runs overview page grouped by family and experiment.""" + """One flat table of every run, newest first. + + This page used to nest runs two deep (approach, then experiment) in + collapsible groups. The hierarchy was real, but it was also the + first thing a reader had to fight: three levels of chrome around the + rows they came for. Approach and experiment are columns now - + visible on every row and typeable into the filter box, which is what + the groups were actually used for. + """ runs = find_runs() - families: Dict[str, Dict[str, List[Dict[str, Any]]]] = {} - for r in runs: - fam, _, rest = r["exp"].partition("/") - families.setdefault(fam, {}).setdefault(rest or fam, []).append(r) summaries = {r["rel"]: run_summary(r["rel"]) or {} for r in runs} - # The task and round columns are laid out once for the whole page, so - # a task's chips line up across runs, experiments, and families. The - # explore/learn column sits to the right of every task column, so it - # can stay per-family without costing any of that alignment - which - # spares families that never explore its reserved width. - page_layout = grid_layout( - [s.get("episodes", []) for s in summaries.values()]) live = live_runs(runs) # A process lsof could not pin owns the newest run of its experiment # and seed: the one it made at startup. Older runs of that key are @@ -2467,57 +2318,36 @@ def index_page() -> str: if not runs: body.append( f"

No run_* directories found under {esc(LOGS_ROOT)}.

") - # Provenance and units live in the header tooltips rather than in - # the labels: the labels are read on every row, the provenance once. - table_head = ( - "runseed" - "status" - "" - "episodes" - "test results" - "" - "coststartedmodified" - "time" - "") - for fam in sorted(families): - exps = families[fam] - fam_runs = [r for rs in exps.values() for r in rs] - fam_misc = grid_layout( - [summaries[r["rel"]].get("episodes", []) for r in fam_runs]) - layout = dict(page_layout, misc=fam_misc["misc"]) - widths = [w or grid_width(layout) for w in RUN_COL_W] - cols = "" + "".join(f"" - for w in widths) + "" - body.append(f"
{esc(fam)} " - f"({len(exps)} experiments, {len(fam_runs)} runs)" - "") - for expname in sorted(exps): - rows = "".join( - run_row(r, summaries[r["rel"]], layout, live, newest[( - r["exp"], r["seed"])][1] == r["rel"]) - for r in exps[expname]) - # Open by default: a first visit should land on the run - # table itself, not on a stack of headers hiding it. A - # reader who collapses one gets that remembered instead - # (restoreGroups reapplies localStorage on load). - body.append(f"
" - f"{esc(expname)} " - f"({len(exps[expname])} runs)" - f"" - f"{cols}{table_head}{rows}
") - body.append("
") + else: + head = ( + "" + "" + "run" + "approach" + "experiment" + "seed" + "status" + "result" + "cost" + "started" + "time" + "") + rows = "".join( + run_row(r, summaries[r["rel"]], live, newest[( + r["exp"], r["seed"])][1] == r["rel"]) for r in runs) + body.append(f"{head}{rows}
") body.append("") - topbar = ("" "" - "" - "" + "newest runs first'>" "{esc(LOGS_ROOT)}") return page("runs - log viewer", topbar, "".join(body)) @@ -2595,51 +2425,96 @@ def replay_caption(ep: Dict[str, Any]) -> Tuple[str, str, str]: return head, why, verdict +def _clip_card(ep: Dict[str, Any], vids: str, run_rel: str) -> str: + """One small captioned player in a round's filmstrip.""" + if ep["kind"] == "test": + what = f"test · task {ep['task']}" + else: + what = "practice" + solved = ep.get("env_solved", ep.get("env_accepted")) + if solved is None: + verdict = "no verdict" + else: + cls, word = ("ok", "solved") if solved else ("bad", "failed") + reward = (f" {ep['env_reward']:.2f}" if "env_reward" in ep else "") + verdict = f"{word}{reward}" + return (f"
{vids}" + f"
{what} · {verdict}
" + f"transcript →
" + "
") + + +def _fit_card(fit: Dict[str, Any]) -> str: + """The sysID verdict that separates one round from the next.""" + rows = "".join(f"
{esc(k)} → " + f"{v:.4g}
" + for k, v in sorted(fit["params"].items())) + refit = (f" ({int(fit['refits'])} fits this " + "cycle; the last one is what the planner carries " + "forward)" if fit["refits"] > 1 else "") + return ("
⚙ What it learned here" + f"{refit}
{rows}
" + "These physical parameters were " + "recovered from the practice episodes above and handed to " + "the planner for the next round.
") + + def replays_page(run_rel: str) -> Optional[str]: - """Every recorded episode of a run, in order, as a scrollable reel. + """A run's whole pipeline as a reel: practice, what it learned, test. The run page pairs each video with its agent transcript, which is - the right thing when reading one episode and the wrong thing when - the question is whether behaviour improved over the run. Here the - clips are simply laid out in time order with the round, the intent, - and the verdict beside each. + right for reading one episode and wrong for the question this + viewer gets asked most - did the robot get better? Here the clips + are small and side by side, grouped into the rounds of the online + loop, with the sysID verdict shown between the rounds it separates. """ run_abs = safe_join(run_rel) if not run_abs or not os.path.isdir(run_abs): return None summary = run_summary(run_rel) assert summary is not None - cards = [] + # Clips grouped by round; a replanned task's two transcripts share + # one video, so identical players are shown once rather than + # reading as two attempts that never happened. + by_round: Dict[int, List[str]] = {} seen: Set[str] = set() for ep in summary["episodes"]: vids = episode_videos(ep, run_rel) - if not vids: - continue - # A replanned task's two transcripts share one video; showing it - # twice would read as two attempts that never happened. - if vids in seen: + if not vids or vids in seen: continue seen.add(vids) - head, why, verdict = replay_caption(ep) - cards.append(f"

{esc(head)}

" - f"
{verdict}
" - f"

{why}

{vids}" - f"read this episode's " - "transcript →
") - if not cards: - cards = [ + rnd = int(ep.get("round", 0)) + by_round.setdefault(rnd, []).append(_clip_card(ep, vids, run_rel)) + fits_by_round: Dict[int, Dict[str, Any]] = { + int(f["after_round"]): f + for f in summary.get("fits", []) + } + out = [] + for rnd in sorted(set(by_round) | set(fits_by_round)): + clips = by_round.get(rnd, []) + strip = ("
" + "".join(clips) + + "
" if clips else + "

No videos recorded for this round.

") + fit = fits_by_round.get(rnd) + band = _fit_card(fit) if fit else "" + out.append(f"

Round {rnd + 1}

" + f"{strip}{band}
") + if not out: + out = [ "

This run recorded no videos. They are " "written when main.py runs with --make_test_videos / " "--make_interaction_videos.

" ] - intro = ("") + intro = ("") body = (f"
{intro}" - f"{''.join(cards)}
") + f"{''.join(out)}") topbar = (f"← run detail" f"{esc(run_rel)}") return page(run_rel + " replays - log viewer", topbar, body) @@ -2827,8 +2702,13 @@ def interaction_videos_for_run( def _video_figure(url: str, label: str) -> str: """One video player with its caption and raw link.""" + # #t=0.1 makes the browser seek there and paint that frame as the + # poster. Without it a preload='metadata' player is a black + # rectangle until someone presses play, which is unreadable on the + # replay reel where a dozen clips sit side by side. return (f"
{label} · " + f"src='{url}#t=0.1'>" + f"
{label} · " f"raw" f"
") From 8b4e97c2c11cbc2f1393cd58ef75e82579baab5d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 25 Aug 2026 14:28:08 -0400 Subject: [PATCH 04/48] log_viewer: full-width table, learning/oracle filter, drop the cost column MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Three things the flat index still got wrong. The table was sized to its content and left most of the window empty; it is the page's content, so it takes the page's width. Cost is gone from the index. It is the agent SDK's valuation of the run's tokens at API rates, which on a subscription bills nothing — so as a column it invited exactly the question it got ("what is the point of showing cost?"). The run page still reports it per episode and in total, where the surrounding detail makes it mean something. Filtering by what an arm IS took knowing that oracle approaches are named oracle_*, and typing it. Each row now carries data-kind, and an all / learning / oracle toggle sits beside the filter box: learning for the agent_* approaches that have to learn a model, oracle for the ones handed the ground truth. It composes with the text filter (both apply) and survives a reload. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/log_viewer.py | 73 ++++++++++++++++++++++++++++++------------- 1 file changed, 52 insertions(+), 21 deletions(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 6d7bdf21d..40bacbfc7 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1695,7 +1695,7 @@ def ansi_to_html(text: str) -> str: font-size: 9px; font-weight: 700; letter-spacing: .1em; text-transform: uppercase; position: sticky; top: 0; z-index: 2; border-bottom: 1px solid var(--border2); } -table.grid.runs { table-layout: fixed; } +table.grid.runs { width: 100%; } tr.runrow:hover > td { background: var(--surface2); } tr.runrow td:nth-child(2) a { color: var(--bright); font-weight: 600; } tr.runrow td:nth-child(2) a:hover { color: var(--accent); } @@ -1795,6 +1795,13 @@ def ansi_to_html(text: str) -> str: a.watchlink:hover { text-decoration: none; filter: brightness(1.1); } .muted { color: var(--muted); font-weight: 400; } +.kindbar { display: inline-flex; border: 1px solid var(--border2); + border-radius: 5px; overflow: hidden; flex-shrink: 0; } +.kindbar button.kindbtn { height: 24px; border: none; border-radius: 0; + border-right: 1px solid var(--border2); } +.kindbar button.kindbtn:last-child { border-right: none; } +.kindbar button.kindbtn.on { background: var(--accent-dim); + color: var(--accent); } .help { display: inline-block; width: 14px; height: 14px; margin-left: 6px; border: 1px solid var(--border2); border-radius: 50%; color: var(--muted2); font: 700 9px/12px var(--sans); text-align: center; cursor: help; @@ -1897,14 +1904,31 @@ def ansi_to_html(text: str) -> str: } // Index page: filter + compare + collapsible groups. -function filterRuns(text) { - text = text.toLowerCase(); - sessionStorage.setItem('lv-filter', text); +function kindFilter() { return sessionStorage.getItem('lv-kind') || 'all'; } +function setKind(k) { + sessionStorage.setItem('lv-kind', k); + paintKindBtns(); + applyFilters(); +} +function paintKindBtns() { + var k = kindFilter(); + $all('button.kindbtn').forEach(function(b) { + b.classList.toggle('on', b.dataset.kind === k); + }); +} +function applyFilters() { + var text = (sessionStorage.getItem('lv-filter') || '').toLowerCase(); + var kind = kindFilter(); $all('.runrow').forEach(function(row) { - row.classList.toggle('hidden', - !!text && row.dataset.key.indexOf(text) === -1); + var hide = (!!text && row.dataset.key.indexOf(text) === -1) || + (kind !== 'all' && row.dataset.kind !== kind); + row.classList.toggle('hidden', hide); }); } +function filterRuns(text) { + sessionStorage.setItem('lv-filter', text); + applyFilters(); +} // Index page: sort runs by seed (the server order) or by start time. // Time mode interleaves each experiment's seeds newest-first; the // experiment and family groups themselves stay in place. @@ -2080,15 +2104,16 @@ def ansi_to_html(text: str) -> str: document.addEventListener('DOMContentLoaded', function() { paintAutoBtn(); paintThemeBtn(); + paintKindBtns(); paintSortBtn(); applySort(); pollStamp(); setInterval(pollStamp, REFRESH_MS); var f = $('#runfilter'); - if (f) { - f.value = sessionStorage.getItem('lv-filter') || ''; - if (f.value) filterRuns(f.value); - } + if (f) f.value = sessionStorage.getItem('lv-filter') || ''; + // Unconditional: a stored kind filter has to be reapplied even when + // the text box is empty. + if ($('.runrow')) applyFilters(); var r = window._restore; if (r) { var sb = $('.sidebar'); @@ -2147,10 +2172,6 @@ def page(title: str, topbar_extra: str, body: str) -> str: "
solved/total on held-out tasks, with the mean reward. " "This is the score. On the domino tasks reward is 1 for a " "solve minus a penalty per domino spent, so 0.90 beats 0.80.
" - "
cost
" - "
What the agent SDK reported those tokens would cost at API " - "rates. On a Claude subscription nothing is billed per query - " - "read it as a usage meter, not a bill.
" "
") @@ -2242,7 +2263,6 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, is_newest: bool) -> str: """One flat row on the index: what it was, how it went, where to look.""" tr_str = test_results_str(summary) or "-" - cost = summary.get("total_cost", 0.0) fmt = "%Y-%m-%d %H:%M" start_ts = _run_start_ts(r["name"], r["mtime"]) sstr = datetime.datetime.fromtimestamp(start_ts).strftime(fmt) @@ -2253,7 +2273,10 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, # Approach and experiment join the filter key, so what used to be a # group heading is now something you type into the filter box. key = f"{r['exp']} {r['seed']} {r['name']} {status}".lower() - cost_str = f"${cost:.2f}" if cost else "-" + # An oracle arm is handed the ground-truth model and learns nothing; + # everything else is a learner. The approach name is the only thing + # that says which, and it says it reliably. + kind = "oracle" if fam.startswith("oracle") else "learning" copy_path = os.path.relpath(os.path.join(LOGS_ROOT, r["rel"])) is_live = status == "running" esc_rel = esc(r["rel"]) @@ -2271,7 +2294,7 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, "\u2715") return ("" + f"data-kind='{kind}' data-start='{start_ts:.0f}'>" f"" "" f"" @@ -2284,7 +2307,6 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, f"{esc(r['seed'])}" f"{status_chip(r, summary, live, is_newest)}{kill_btn}" f"{esc(tr_str)}" - f"{cost_str}" f"{sstr}" f"{dur_str}") @@ -2332,9 +2354,6 @@ def index_page() -> str: "status" "result" - "cost" "started" "time" "") @@ -2345,6 +2364,18 @@ def index_page() -> str: body.append("") topbar = ("" + "" + "" + "" + "" + "" "" From 589ef5f6f7c5cb74257c8a9a7863e9951386e29d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 25 Aug 2026 14:31:36 -0400 Subject: [PATCH 05/48] log_viewer: name the three pages, and click a run to see the robot MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit "Run detail" told a reader nothing, so following it out of the replay reel landed them back on the densest page in the viewer with no idea why. There are only three pages here and each nav link now names its destination: all runs the index ▶ watch that run's episodes, as video logs that run's transcripts, logs and files The run name on the index points at the reel now, not at the log page: "show me this run" should show the robot, and the transcripts are a secondary "logs" link on the same row. Both run-scoped pages carry a link back to the index and across to each other, so neither is a dead end, and the log page's duplicate banner button is gone. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/log_viewer.py | 25 +++++++++++++++++-------- 1 file changed, 17 insertions(+), 8 deletions(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 40bacbfc7..36411c71a 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1785,6 +1785,10 @@ def ansi_to_html(text: str) -> str: .fitvals { display: flex; flex-wrap: wrap; gap: 6px 18px; margin: 6px 0; } .fitvals code { color: var(--muted); } .fitvals b { font-family: var(--mono); color: var(--bright); } +a.logslink { margin-left: 10px; font-size: 10px; color: var(--muted2); + text-transform: uppercase; letter-spacing: .08em; white-space: nowrap; } +tr.runrow:hover a.logslink, .topbar a.logslink:hover { + color: var(--accent); } a.playlink { margin-left: 6px; color: var(--muted2); font-size: 11px; } tr.runrow:hover a.playlink { color: var(--accent); } a.playlink:hover { text-decoration: none; } @@ -2158,8 +2162,8 @@ def page(title: str, topbar_extra: str, body: str) -> str: "
" "
run
" "
One execution of main.py, named by when it " - "started. Click it for the transcripts and files; click " - "▶ watch for the videos.
" + "started. Click it (or ▶ watch) to see the robot; " + "logs opens the transcripts and files instead." "
approach
" "
The method. An oracle_* approach is handed the " "ground-truth model and learns nothing - it is an upper bound. An " @@ -2299,7 +2303,9 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, "" f"" "▶ watch" - f"{esc(r['name'])}" + f"{esc(r['name'])}" + f"logs" f"{del_btn}" f"{esc(fam)}" @@ -2546,7 +2552,9 @@ def replays_page(run_rel: str) -> Optional[str]: body = (f"
{intro}" f"{''.join(out)}
") - topbar = (f"← run detail" + topbar = ("← all runs" + f"" + "logs & transcripts" f"{esc(run_rel)}") return page(run_rel + " replays - log viewer", topbar, body) @@ -2616,16 +2624,17 @@ def run_page(run_rel: str) -> Optional[str]: "overflow:hidden'>" f"" f"

Select a file.

" "
") - topbar = f"{esc(run_rel)}" - return page(run_rel + " - log viewer", topbar, body) + topbar = ("← all runs" + f"" + "▶ watch videos" + f"{esc(run_rel)}") + return page(run_rel + " logs - log viewer", topbar, body) # ------------------------------------------------------------- fragments From 5ccb3c419d76db7667ea172296096f20a1828abc Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 12:45:57 -0400 Subject: [PATCH 06/48] domino: a fan env with no ball, blowing the start domino directly pybullet_domino_fan put a ball between the fan and the dominoes: the wind pushed the ball and the ball knocked the chain over. The task we want is the fan acting on the dominoes themselves, so that arranging the layout is the whole problem and the robot never pushes anything. The ball variant is preserved under pybullet_domino_fan_ball, and the bare name now builds domino + fan only. Which body the wind pushes is the entire difference between the two tasks, so the name says it. Wind targeting had to move. With a ball there is one target body for the life of the env, wired once at startup. The start domino is not like that: roles are colours assigned per layout, so the green block is a different body id from one task to the next. _wire_wind_target re-resolves it on every reset, after the components have taken the new state, and leaves the previous target alone (with a warning) if a task has no start block. It also skips dominoes absent from the state - the component allocates num_dominos_max bodies once and a task instantiates a subset, so reading a colour off the tail of that list raises. envs/all.yaml gains a parked domino_fan entry. Its comment records what the config cannot: these tasks carry NO DominoEvaluator, because the generator attaches it only when dominoes are the sole dynamic component. Reward is plain goal-reached with no per-blue cost, so its numbers are not comparable with a domino_* arm's. Verified: the env builds with [DominoComponent, FanComponent] and no ball, generates a Toppled(target) task, and after reset the fan's wind target is the green start block's body id. pybullet_domino_fan_ball still resolves and keeps its ball. tests/envs/test_pybullet_domino_ composed.py passes. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- predicators/envs/pybullet_domino/env.py | 84 ++++++++++++++++++++-- scripts/configs/predicatorv3/envs/all.yaml | 26 +++++++ 2 files changed, 106 insertions(+), 4 deletions(-) diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index d3e401fcc..5e7b3be1f 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -405,7 +405,9 @@ def _store_pybullet_bodies(self, pybullet_bodies: Dict[str, Any]) -> None: comp_bodies = comp.initialize_pybullet(self._physics_client_id) comp.store_pybullet_bodies(comp_bodies) - # Wire up fan -> ball connection if both present + # Wire up fan -> ball connection if both present. Without a + # ball the target is a domino, which changes per task, so it is + # wired on every reset by _wire_wind_target instead. if self._fan_component is not None and self._ball_component is not None: self._fan_component.set_wind_target(self._ball_component.ball_id) @@ -447,6 +449,42 @@ def _set_domain_specific_state(self, state: State) -> None: if self._ball_component is not None: self._ball_component.set_current_state(state) + self._wire_wind_target(state) + + def _wire_wind_target(self, state: State) -> None: + """Point the fan at the body it is supposed to blow, for this task. + + With a ball in the scene the fan blows the ball, which is one + body for the life of the env and is wired at startup. Without + one it blows the START domino, and which body holds that role is + a property of the task, not of the env: the roles are colours + assigned per layout, so the green block is a different body id + from one reset to the next. Re-resolving here - after the + components have taken the new state - is what keeps the wind on + the domino the task actually starts from. + + A task with no start block (none generated, or a scene where the + chain begins elsewhere) leaves the previous target in place + rather than silently blowing an arbitrary body. + """ + if self._fan_component is None or self._ball_component is not None: + return + if self._domino_component is None: + return + for domino in self._domino_component.dominos: + # The component allocates num_dominos_max bodies once and a + # task instantiates a subset of them, so the tail of this + # list is absent from the state; reading a colour off one + # raises rather than returning False. + if domino.id is None or domino not in state: + continue + # pylint: disable-next=protected-access + if DominoComponent._StartBlock_holds(state, [domino]): + self._fan_component.set_wind_target(domino.id) + return + logging.warning( + "Fan env has no start (green) domino in this task; leaving the " + "wind target unchanged.") def _domain_specific_step(self) -> None: """Run component physics updates (e.g., fan wind simulation).""" @@ -911,8 +949,13 @@ def get_name(cls) -> str: return "pybullet_domino" -class PyBulletDominoFanEnv(PyBulletDominoComposedEnv): - """Backward-compatible domino + fan + ball environment class.""" +class PyBulletDominoFanBallEnv(PyBulletDominoComposedEnv): + """Domino + fan + ball: the fan blows the BALL, which knocks dominoes. + + Formerly ``pybullet_domino_fan``. Renamed when the ball-free variant + below took that name, because which body the wind pushes is the + whole difference between the two tasks and the old name did not say. + """ def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: bounds = self._default_workspace_bounds() @@ -926,6 +969,34 @@ def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: use_gui=use_gui, **kwargs) + @classmethod + def get_name(cls) -> str: + return "pybullet_domino_fan_ball" + + +class PyBulletDominoFanEnv(PyBulletDominoComposedEnv): + """Domino + fan, no ball: the fan blows the START DOMINO directly. + + The task is to arrange the dominoes so that switching the fan on + topples the chain all the way to the target - the robot never + pushes anything itself, so the whole plan is in the layout. + + Wind targets the green start block rather than the ball, resolved on + every reset (see ``_wire_wind_target``): which body carries the + start role changes from task to task, so it cannot be wired once at + startup the way the ball's single body can. + """ + + def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: + bounds = self._default_workspace_bounds() + domino_comp = self._make_domino_component(bounds) + fan_comp = FanComponent(workspace_bounds=bounds, + table_height=self.table_height, + table_width=self.table_width) + super().__init__(components=[domino_comp, fan_comp], + use_gui=use_gui, + **kwargs) + @classmethod def get_name(cls) -> str: return "pybullet_domino_fan" @@ -995,7 +1066,8 @@ def get_name(cls) -> str: from predicators import utils # Choose which environment to test - # Options: "domino", "domino_fan", "domino_fan_ramp", + # Options: "domino", "domino_fan", "domino_fan_ball", + # "domino_fan_ramp", # "domino_fan_ramp_stairs" # Change this to test different environments test_env = "domino_fan_ramp_stairs" @@ -1037,6 +1109,10 @@ def get_name(cls) -> str: print("Creating PyBulletDominoFanEnv...") CFG.env = "pybullet_domino_fan" demo_env = PyBulletDominoFanEnv(use_gui=True) + elif test_env == "domino_fan_ball": + print("Creating PyBulletDominoFanBallEnv...") + CFG.env = "pybullet_domino_fan_ball" + demo_env = PyBulletDominoFanBallEnv(use_gui=True) elif test_env == "domino_fan_ramp": print("Creating PyBulletDominoFanRampEnv...") CFG.env = "pybullet_domino_fan_ramp" diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index a685903a7..bbc13efc3 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -336,6 +336,32 @@ ENVS: domino_heavy_block_tasks: True domino_min_block_num_blues: 4 domino_test_turn_ratio: 1.0 + # Domino + fan, no ball: the fan blows the START (green) domino, so the + # task is a pure layout problem - arrange the chain so that switching the + # fan on topples the target. The robot never pushes anything itself. + # + # NOTE: unlike the plain domino envs, these tasks carry NO DominoEvaluator + # (the task generator attaches it only when dominoes are the sole dynamic + # component). Reward is therefore plain goal-reached, with no per-blue + # cost - do not compare its rewards against a domino_* arm's. + domino_fan: + NAME: "pybullet_domino_fan" + SKIP: True + FLAGS: + max_initial_demos: 0 + excluded_objects_in_state_str: "loc,rot,angle,direction" + horizon: 500 + domino_initialize_at_finished_state: False + domino_use_domino_blocks_as_target: True + domino_use_continuous_place: True + process_planning_heuristic_weight: 2.0 + domino_has_glued_dominos: False + keep_failed_demos: True + predicate_invent_invent_derived_predicates: True + pybullet_birrt_extend_num_interp: 20 + pybullet_birrt_path_subsample_ratio: 2 + fan_known_controls_relation: True + fan_fans_blow_opposite_direction: False # coffee: # NAME: "pybullet_coffee" # FLAGS: From 227d9b366adf93d5ed1787539ff7f3d6b4992e85 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 13:13:59 -0400 Subject: [PATCH 07/48] domino_fan: make the wind actually topple the start domino Probing the new ball-free fan env turned up three separate reasons a wind-started chain could not work. None was the force constant, which was the thing I expected to be tuning. 1. Layouts were not aligned to any fan. The generator picks the start pose uniformly and the travel direction from {+y, +x, -x} at random, which is right for a chain the ROBOT pushes and wrong for one the WIND starts: a chain crossing the airstream cannot cascade however hard the fan blows, so the task was unsolvable before a planner saw it. domino_fan_aligned_tasks lays the chain along a chosen fan's axis, start block in the upwind fifth, and records the side in last_fan_side. The two frames meet in exactly one table: _FAN_SIDE_TO_ROTATION reconciles the fan's yaw with the generator's travel convention (sin/cos of rotation, measured from +y). 2. Wind pushed through the centre of mass. A force through the centre is pure translation: the domino slid across the table indefinitely and never tipped. Applied 0.4 of its height above the origin, the same 2 N makes a moment about the bottom edge and it falls. 3. The wind never stopped. Even once down, the fan went on shoving the fallen block, sliding it the full 0.29 m into the target - a bridge task "solved" with none of the bridge built, at any force. The fan now cuts out when its target passes the same 10-degree threshold the Toppled predicate uses, so the physics stops where the symbol flips. A fallen domino lies out of the airstream; this is what that means. Both new behaviours are opt-in arguments to set_wind_target defaulting off, so pybullet_domino_fan_ball keeps a centred, never-ending push on its ball - verified: z_offset 0.0, stop_when_toppled False, target is still the ball body. Measured, 5 aligned test tasks at the default force: the wind now tips the start block to +-90 in every one, and reaches the goal in none of them. That is the correct answer, not a regression - start and target sit 0.29 m apart against a ~0.15 m topple reach, with blue blocks parked to the side, because these are bridge tasks. The wind starts the chain; building the rest is the robot's job. Before fix 3 they scored 4/5 by bulldozing. Still open: pybullet_domino_fan appears in no ground-truth registry (options, processes, predicates, types, gt_simulator), so the oracle arm raises NotImplementedError and cannot yet be run as a baseline. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../components/fan_component.py | 61 +++++++++++++++++-- predicators/envs/pybullet_domino/env.py | 9 ++- .../task_generators/domino_task_generator.py | 60 ++++++++++++++++-- predicators/settings.py | 10 +++ 4 files changed, 130 insertions(+), 10 deletions(-) diff --git a/predicators/envs/pybullet_domino/components/fan_component.py b/predicators/envs/pybullet_domino/components/fan_component.py index 7cb7f0e66..06afb537d 100644 --- a/predicators/envs/pybullet_domino/components/fan_component.py +++ b/predicators/envs/pybullet_domino/components/fan_component.py @@ -156,6 +156,15 @@ def __init__(self, # Object to apply wind force to (set by composed environment) self._wind_target_id: Optional[int] = None + # Height above the target's origin at which the wind pushes. Zero + # for a ball, where a force through the centre is what rolls it. + # A domino needs a positive value or it never tips: see + # _apply_wind_force. + self._wind_target_z_offset: float = 0.0 + # Stop pushing the target once it has fallen over (see + # _target_still_standing). Only meaningful for a body that can + # fall out of the airstream, so it travels with the z offset. + self._wind_stops_when_toppled: bool = False # ------------------------------------------------------------------------- # DominoEnvComponent interface implementation @@ -314,7 +323,9 @@ def step(self) -> None: physicsClientId=self._physics_client_id, ) # Apply force to wind target (e.g., ball) - if self._wind_target_id is not None: + if self._wind_target_id is not None and ( + not self._wind_stops_when_toppled + or self._target_still_standing(self._wind_target_id)): self._apply_wind_force(fan_obj.fan_ids[0], self._wind_target_id) else: @@ -335,9 +346,41 @@ def step(self) -> None: # Fan-specific methods # ------------------------------------------------------------------------- - def set_wind_target(self, target_id: int) -> None: - """Set the object that wind forces should be applied to.""" + def set_wind_target(self, + target_id: int, + z_offset: float = 0.0, + stop_when_toppled: bool = False) -> None: + """Set the object that wind forces should be applied to. + + ``z_offset`` raises the point of application above the target's + origin, which is what turns a shove into a topple for a body + that stands on a narrow base. ``stop_when_toppled`` cuts the + force once the target is down. + """ self._wind_target_id = target_id + self._wind_target_z_offset = z_offset + self._wind_stops_when_toppled = stop_when_toppled + + def _target_still_standing(self, target_id: int) -> bool: + """Is the wind target still upright enough to be pushed? + + A standing domino presents its face to the airstream; a fallen + one lies flat on the table, out of it. Without this the fan goes + on shoving a body that is already down and slides it across the + table indefinitely - which lets the start block bulldoze all the + way into the target and "solve" a bridge task with none of the + bridge built. + + Uses the same 10-degree threshold as the Toppled predicate, so + the physics stops exactly where the symbol flips. + """ + _, orn = p.getBasePositionAndOrientation( + target_id, physicsClientId=self._physics_client_id) + roll = p.getEulerFromQuaternion(orn)[0] + # Fold to [-pi/2, pi/2): a box turned 180 degrees about its own + # width axis is the same box, so roll only means anything mod pi. + roll = (roll + np.pi / 2) % np.pi - np.pi / 2 + return abs(roll) < np.radians(10.0) def _apply_wind_force(self, fan_id: int, target_id: int) -> None: """Apply wind force from fan to target object.""" @@ -353,11 +396,21 @@ def _apply_wind_force(self, fan_id: int, target_id: int) -> None: world_dir = rmat.dot(local_dir) pos_target, _ = p.getBasePositionAndOrientation( target_id, physicsClientId=self._physics_client_id) + # Apply the force ABOVE the centre of mass, not through it. A + # force through the centre is pure translation: a domino under + # it slides across the table indefinitely and never tips, which + # is how a wind-driven task ends up "solved" by the start block + # bulldozing into the target with the chain untouched. Offset + # upward, the same force makes a moment about the domino's + # bottom edge and it falls. Zero for a ball, whose existing + # behaviour (roll from a central push) is what that task wants. + pos_apply = (pos_target[0], pos_target[1], + pos_target[2] + self._wind_target_z_offset) force_vec = self.wind_force_magnitude * world_dir p.applyExternalForce(objectUniqueId=target_id, linkIndex=-1, forceObj=force_vec.tolist(), - posObj=pos_target, + posObj=pos_apply, flags=p.WORLD_FRAME, physicsClientId=self._physics_client_id) diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index 5e7b3be1f..814eb2662 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -480,7 +480,14 @@ def _wire_wind_target(self, state: State) -> None: continue # pylint: disable-next=protected-access if DominoComponent._StartBlock_holds(state, [domino]): - self._fan_component.set_wind_target(domino.id) + # Push near the top of the domino so the wind tips it + # rather than sliding it: 0.4 of its height above the + # origin is comfortably above the centre and still on + # the body. + self._fan_component.set_wind_target( + domino.id, + z_offset=0.4 * self._domino_component.domino_height, + stop_when_toppled=True) return logging.warning( "Fan env has no start (green) domino in this task; leaving the " diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index a280a4a72..40a3bf237 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -1,6 +1,6 @@ """Task generator for domino-based tasks.""" -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, ClassVar, Dict, List, Optional, Tuple import numpy as np @@ -39,6 +39,9 @@ def __init__(self, self.robot = robot self.robot_init_state = robot_init_state self.additional_components = additional_components or [] + # Fan side the last generated chain was aligned to (None when + # domino_fan_aligned_tasks is off, or before the first chain). + self.last_fan_side: Optional[int] = None def generate_tasks( self, @@ -319,6 +322,45 @@ def _generate_min_block_task( goal_atoms, goal_nl=goal_text.MIN_BLOCK_GOAL_NL) + # A chain's travel direction is (sin rotation, cos rotation) -- see + # _place_straight_domino -- so rotation is measured from +y, turning + # toward +x. A fan on side_idx blows along its own yaw, world + # (cos yaw, sin yaw): left(0) +x, right(1) -x, back(2) +y, + # front(3) -y. These are those two conventions reconciled, which is + # the only place the fan's frame and the chain's frame meet. + _FAN_SIDE_TO_ROTATION: ClassVar[Dict[int, float]] = { + 0: np.pi / 2, # left fan blows +x + 1: -np.pi / 2, # right fan blows -x + 2: 0.0, # back fan blows +y + 3: np.pi, # front fan blows -y + } + + def _fan_aligned_start(self, rng: np.random.Generator, x_lb: float, + x_ub: float, y_lb: float, + y_ub: float) -> Tuple[float, float, float, int]: + """Start pose and travel direction for a wind-started chain. + + Picks a fan side, points the chain downwind, and puts the start + block in the upwind fifth of that axis so the rest of the chain + has the workspace to run into. Free across the crosswind axis: + the wind is uniform, so where the chain sits sideways does not + change whether it cascades, and varying it keeps the task set + from collapsing onto one line. + """ + side = int(rng.integers(0, 4)) + rotation = self._FAN_SIDE_TO_ROTATION[side] + dx, dy = np.sin(rotation), np.cos(rotation) + lead = 0.2 # fraction of the axis reserved upwind of the start + if abs(dx) > abs(dy): # travelling along x + x = (x_lb + lead * (x_ub - x_lb) if dx > 0 else x_ub - lead * + (x_ub - x_lb)) + y = rng.uniform(y_lb, y_ub) + else: # travelling along y + x = rng.uniform(x_lb, x_ub) + y = (y_lb + lead * (y_ub - y_lb) if dy > 0 else y_ub - lead * + (y_ub - y_lb)) + return x, y, rotation, side + def _generate_domino_sequence(self, rng: np.random.Generator, n_dominos: int, @@ -351,10 +393,18 @@ def _generate_domino_sequence(self, def _in_bounds(nx: float, ny: float) -> bool: return x_lb < nx < x_ub and y_lb < ny < y_ub - # Initial position and orientation - x = rng.uniform(x_lb, x_ub) - y = rng.uniform(y_lb, y_ub) - rotation = rng.choice([0, np.pi / 2, -np.pi / 2]) + # Initial position and orientation. A wind-started chain has to + # run downwind from the upwind edge; a robot-pushed one can start + # anywhere and face any of three ways (the fourth, -y, has never + # been in this list). + self.last_fan_side = None + if CFG.domino_fan_aligned_tasks: + x, y, rotation, self.last_fan_side = self._fan_aligned_start( + rng, x_lb, x_ub, y_lb, y_ub) + else: + x = rng.uniform(x_lb, x_ub) + y = rng.uniform(y_lb, y_ub) + rotation = rng.choice([0, np.pi / 2, -np.pi / 2]) gap = self.domino.pos_gap # Place first domino (start block) diff --git a/predicators/settings.py b/predicators/settings.py index 9b5a1d837..dc485a7ac 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -863,6 +863,16 @@ class GlobalSettings: # domino_min_block_num_blues); domino_min_block_tasks does not also # need to be set. domino_heavy_block_tasks = False + # Lay each chain out along one fan's wind axis, starting at the upwind + # end, for the ball-free pybullet_domino_fan env. Off, the generator + # picks the start pose uniformly and the travel direction at random, + # which is right for a task the ROBOT pushes and wrong for one the WIND + # starts: a chain crossing the wind cannot cascade no matter how hard + # the fan blows, so the task is unsolvable before a planner sees it. + # The chosen side is recorded per task in offline_task_metrics + # ("fan_side"), 0=left 1=right 2=back 3=front, matching + # FanComponent's side_idx. + domino_fan_aligned_tasks = False # burger env parameters burger_render_set_of_marks = True From c661af888d1389a95b17c4ff55c557b517a48be2 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 13:29:46 -0400 Subject: [PATCH 08/48] domino_fan: calibrate the wind force, and prove the cascade runs The 2.0 N the fan blew with was the ball's number, inherited by the domino env's copy of FanComponent and never checked against a domino. A 100 g domino 15 mm thick and 150 mm tall, pushed at 0.4 of its height, tips when the wind's moment beats gravity's about the bottom edge: m*g*(depth/2)/(0.4*height) = 0.123 N. A sweep over real tasks agrees, and shows what the extra order of magnitude was buying: force tipped mean displacement 0.05 1/4 15 mm 0.10 4/4 82 mm 0.20 4/4 85 mm 0.30 4/4 83 mm 0.50 4/4 130 mm 1.00 4/4 833 mm 4.00 4/4 5462 mm ~85 mm is one topple of a 150 mm block, so 0.1-0.3 N is the band where the domino falls over and nothing else happens. Past it the block stops toppling and starts flying. domino_fan_wind_force = 0.2 sits ~60% over the threshold, the margin pybullet_fan's 0.06 N keeps over its ball's stiction - same method, different body. The force is a per-target override on set_wind_target, not a change to the class default, so pybullet_domino_fan_ball keeps blowing its ball at 2.0 N. Verified: that env's override is None. And the mechanism works end to end. The generated tasks are BRIDGE tasks - start and target sit further apart than one topple reach on purpose - so they cannot show whether wind can run a chain. Laying one chain by hand at the env's own pos_gap and switching on the fan it faces: 4/4 dominoes topple, purely from wind, no robot involved. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../pybullet_domino/components/fan_component.py | 16 +++++++++++++--- predicators/envs/pybullet_domino/env.py | 3 ++- predicators/settings.py | 11 +++++++++++ 3 files changed, 26 insertions(+), 4 deletions(-) diff --git a/predicators/envs/pybullet_domino/components/fan_component.py b/predicators/envs/pybullet_domino/components/fan_component.py index 06afb537d..eebc89592 100644 --- a/predicators/envs/pybullet_domino/components/fan_component.py +++ b/predicators/envs/pybullet_domino/components/fan_component.py @@ -165,6 +165,9 @@ def __init__(self, # _target_still_standing). Only meaningful for a body that can # fall out of the airstream, so it travels with the z offset. self._wind_stops_when_toppled: bool = False + # Per-target force override (N). None keeps the class default, + # which is calibrated for the ball. + self._wind_force_override: Optional[float] = None # ------------------------------------------------------------------------- # DominoEnvComponent interface implementation @@ -349,17 +352,21 @@ def step(self) -> None: def set_wind_target(self, target_id: int, z_offset: float = 0.0, - stop_when_toppled: bool = False) -> None: + stop_when_toppled: bool = False, + force: Optional[float] = None) -> None: """Set the object that wind forces should be applied to. ``z_offset`` raises the point of application above the target's origin, which is what turns a shove into a topple for a body that stands on a narrow base. ``stop_when_toppled`` cuts the - force once the target is down. + force once the target is down. ``force`` overrides the class + magnitude, which is calibrated for the ball and is 16x what a + domino needs. """ self._wind_target_id = target_id self._wind_target_z_offset = z_offset self._wind_stops_when_toppled = stop_when_toppled + self._wind_force_override = force def _target_still_standing(self, target_id: int) -> bool: """Is the wind target still upright enough to be pushed? @@ -406,7 +413,10 @@ def _apply_wind_force(self, fan_id: int, target_id: int) -> None: # behaviour (roll from a central push) is what that task wants. pos_apply = (pos_target[0], pos_target[1], pos_target[2] + self._wind_target_z_offset) - force_vec = self.wind_force_magnitude * world_dir + magnitude = (self.wind_force_magnitude + if self._wind_force_override is None else + self._wind_force_override) + force_vec = magnitude * world_dir p.applyExternalForce(objectUniqueId=target_id, linkIndex=-1, forceObj=force_vec.tolist(), diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index 814eb2662..6c089f78f 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -487,7 +487,8 @@ def _wire_wind_target(self, state: State) -> None: self._fan_component.set_wind_target( domino.id, z_offset=0.4 * self._domino_component.domino_height, - stop_when_toppled=True) + stop_when_toppled=True, + force=CFG.domino_fan_wind_force) return logging.warning( "Fan env has no start (green) domino in this task; leaving the " diff --git a/predicators/settings.py b/predicators/settings.py index dc485a7ac..cb136b82c 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -873,6 +873,17 @@ class GlobalSettings: # ("fan_side"), 0=left 1=right 2=back 3=front, matching # FanComponent's side_idx. domino_fan_aligned_tasks = False + # Wind force (N) the fan applies to the start domino in the ball-free + # pybullet_domino_fan env. NOT the FanComponent class default (2.0), + # which is the ball's: a 100 g domino 15 mm thick and 150 mm tall, + # pushed at 0.4 of its height, tips at m*g*(depth/2)/(0.4*height) = + # 0.123 N, and a measured sweep agrees - 0.05 N tips 1 of 4 tasks, + # 0.10 N tips 4 of 4. Above ~0.3 N the block stops toppling and + # starts flying: mean displacement is ~85 mm (one topple of a 150 mm + # block) from 0.1 to 0.3 N, 833 mm at 1.0 N and 5.5 m at 4.0 N. + # 0.2 N sits ~60% over the threshold, the same margin + # pybullet_fan's 0.06 N keeps over its ball's stiction. + domino_fan_wind_force = 0.2 # burger env parameters burger_render_set_of_marks = True From a5a9b783d61e5e796b7f876474513fbf52c66503 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 13:46:35 -0400 Subject: [PATCH 09/48] domino_fan: a GT residual simulator, modelled on the fan env's fan/gt_simulator.py is the right template: one rule, the wind, with the force as a FITTED parameter, because the wind is precisely what the agent is supposed to discover. This is that for the ball-free domino env. It is much smaller than the fan env's, and the reason is structural. pybullet_fan needs a grid abstraction because a ball's whole trajectory is wind-driven, cell by cell. Only a domino chain's FIRST block is; the topple, the contact, and the entire cascade are the engine's, and the existing domino processes already describe them. So the program is one rule that pushes the start block, and nothing about chains at all. Two things differ from the ball's wind, both forced by a domino standing on a narrow base rather than rolling: * the push has to act ABOVE the centre of mass, or it translates the block instead of tipping it. ApplyForce is centre-of-mass only by construction, so the offset goes in the way statics decomposes it - the same force at the centre, plus the moment it would have made, r x F with r = (0, 0, lever) giving (-lever*Fy, lever*Fx, 0). That is a torque about the axis perpendicular to the wind: the tipping moment. Both commands are in the vocabulary an agent-synthesized simulator already has, so nothing here is privileged; * the wind stops when its target falls, at the same 10-degree threshold the Toppled predicate uses, so the dynamics stop where the symbol flips. wind_force (0.2 N) and wind_lever (0.06 m) are both fitted, not fixed: neither is observable from a single state, and recovering them from watching dominoes fall is the system-ID task. roll joins x/y in RESIDUAL_FEATURES because the effect is a topple, not a shove - a program with the force right and the lever wrong slides the block, and only roll separates the two. Verified: get_gt_simulator("pybullet_domino_fan") resolves; the rule emits ApplyForce (0.2, 0, 0) and ApplyTorque (0, 0.012, 0) = lever x force with the left fan on; four fans on emit nothing, matching the physics, which needed a tolerance - they cancel to ~1e-16, not to zero, and an exact test let that dust through as a command every step. pybullet_domino and pybullet_fan still resolve their own. Still missing for an oracle run: types, predicates, options and processes for this env. This is the world model, not the planner. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../domino_fan/__init__.py | 7 + .../domino_fan/gt_simulator.py | 186 ++++++++++++++++++ 2 files changed, 193 insertions(+) create mode 100644 predicators/ground_truth_models/domino_fan/__init__.py create mode 100644 predicators/ground_truth_models/domino_fan/gt_simulator.py diff --git a/predicators/ground_truth_models/domino_fan/__init__.py b/predicators/ground_truth_models/domino_fan/__init__.py new file mode 100644 index 000000000..e4c8aefc3 --- /dev/null +++ b/predicators/ground_truth_models/domino_fan/__init__.py @@ -0,0 +1,7 @@ +"""Ground-truth models for the ball-free domino-fan environment.""" + +from .gt_simulator import PyBulletDominoFanGroundTruthSimulatorFactory + +__all__ = [ + "PyBulletDominoFanGroundTruthSimulatorFactory", +] diff --git a/predicators/ground_truth_models/domino_fan/gt_simulator.py b/predicators/ground_truth_models/domino_fan/gt_simulator.py new file mode 100644 index 000000000..ad9114177 --- /dev/null +++ b/predicators/ground_truth_models/domino_fan/gt_simulator.py @@ -0,0 +1,186 @@ +"""Ground-truth simulator program for pybullet_domino_fan residual dynamics. + +The counterpart of ``fan/gt_simulator.py`` for the ball-free domino-fan +env: while a fan is on, the rule pushes the START domino along that +fan's facing direction and the base sim's engine owns everything +downstream -- the topple, the contact with the next block, the whole +cascade. Only the first block is wind-driven, which is what makes this +program so much smaller than the grid machinery ``pybullet_fan`` needs +for a ball whose entire trajectory is wind. + +Two things differ from the ball's wind, and both are forced by the fact +that a domino stands on a narrow base rather than rolling. + +**The push has to act above the centre of mass.** A force through the +centre is pure translation: the domino slides across the table +indefinitely and never tips, which is how a start block ends up +bulldozing into the target and "solving" a bridge task with none of the +bridge built. ``ApplyForce`` is centre-of-mass only by construction, so +the offset is expressed the way statics says it decomposes -- the same +force at the centre, plus the moment it would have made about it: + + r x F with r = (0, 0, lever), F = (Fx, Fy, 0) + = (-lever * Fy, lever * Fx, 0) + +a torque about the horizontal axis perpendicular to the wind, which is +exactly the tipping moment. Emitting both is equivalent to applying the +force at ``lever`` above the origin, and keeps the whole dynamic inside +the residual-command vocabulary an agent-synthesized simulator can use. + +**The wind stops when its target falls.** A standing domino presents +its face to the airstream; a fallen one lies flat, out of it. Without +this the fan goes on shoving a body that is already down. The 10-degree +threshold is the ``Toppled`` predicate's, so the dynamics stop exactly +where the symbol flips. + +Both constants are fitted parameters, not fixed geometry: ``wind_force`` +and ``wind_lever`` are what a system-ID pass has to recover from watching +dominoes fall, and neither is observable from a single state. + +Because commands act through engine stepping, this artifact is scored +and fit by free-running rollout matching (``has_physics_rules`` +routing), never teacher-forced. +""" + +from __future__ import annotations + +from typing import Dict, List + +import numpy as np + +from predicators.code_sim_learning.commands import CommandBuffer +from predicators.code_sim_learning.fit_space import ParamSpec +from predicators.code_sim_learning.utils import Params, ResidualUpdate, \ + objs_by_type +from predicators.ground_truth_models import GroundTruthSimulatorFactory +from predicators.settings import CFG +from predicators.structs import Object, State + +# ── Constants ──────────────────────────────────────────────────── + +# Held-mode force (N), equal to the env's domino_fan_wind_force. A 100 g +# domino 15 mm thick and 150 mm tall, pushed at 0.4 of its height, tips +# at m*g*(depth/2)/(0.4*height) = 0.123 N; this sits ~60% over that, the +# same margin the ball's 0.06 N keeps over its stiction. Fitted init. +WIND_FORCE = 0.2 + +# Height above the domino's origin the wind effectively acts at (m), +# 0.4 * domino_height. Fitted init: the real lever depends on where the +# airstream meets the face, which no state feature reports. +WIND_LEVER = 0.06 + +# Past this roll the domino is down and out of the airstream. The +# Toppled predicate's threshold, deliberately. +TOPPLED_DEG = 10.0 + +# Below this the summed wind is nothing (N). Opposing fans cancel to +# floating-point dust rather than to exact zero -- four fans at 0.2 N +# sum to ~1e-16 -- and an exact == 0.0 test lets that dust through as a +# force and torque command on every step of every action. +WIND_EPS = 1e-9 + + +def _is_start_block(state: State, domino: Object) -> bool: + """Green means the chain starts here. + + Roles in this env are colours, not a feature: the generator paints + the start block and the classifiers read it back. Kept in sync with + ``DominoComponent._StartBlock_holds`` by construction -- both + compare the same three channels against the same constant. + """ + from predicators.envs.pybullet_domino.components.domino_component import \ + DominoComponent # pylint: disable=import-outside-toplevel + eps = 1e-3 + return all( + abs( + float(state.get(domino, c)) - + DominoComponent.start_domino_color[i]) < eps + for i, c in enumerate(("r", "g", "b"))) + + +def _upright(state: State, domino: Object) -> bool: + """Still standing, so still in the wind. + + Roll is only meaningful modulo pi -- a box turned 180 degrees about + its own width axis is the same box -- so it is folded before being + compared, exactly as the env's own check does it. + """ + roll = float(state.get(domino, "roll")) + roll = (roll + np.pi / 2) % np.pi - np.pi / 2 + return abs(roll) < np.radians(TOPPLED_DEG) + + +def _wind_topples_start_block(state: State, updates: ResidualUpdate, + params: Params, + cmds: CommandBuffer) -> ResidualUpdate: + """Each on-fan pushes the standing start domino along its local +X. + + Simultaneous fans sum as force vectors, so two facing each other + cancel to nothing -- the physical reading of ``pybullet_fan``'s + ``FanOn(f) and FanOff(opposite(f))`` precondition, and the reason a + plan that switches on both sides moves nothing at all. + """ + objs = objs_by_type(state) + dominos = objs.get("domino", []) + if not dominos: + return updates + + sign = -1.0 if CFG.fan_fans_blow_opposite_direction else 1.0 + fx = fy = 0.0 + for fan in objs.get("fan", []): + if state.get(fan, "is_on") <= 0.5: + continue + rot = float(state.get(fan, "rot")) + fx += sign * float(np.cos(rot)) * params["wind_force"] + fy += sign * float(np.sin(rot)) * params["wind_force"] + if abs(fx) < WIND_EPS and abs(fy) < WIND_EPS: + return updates + + lever = params["wind_lever"] + for domino in dominos: + if not _is_start_block(state, domino) or not _upright(state, domino): + continue + cmds.apply_force(domino, (fx, fy, 0.0)) + # The moment that same force would have made about the centre + # had it been applied ``lever`` higher up; see the module + # docstring for the cross product. + cmds.apply_torque(domino, (-lever * fy, lever * fx, 0.0)) + return updates + + +# ── Public API: consumed by read_simulator_components ──────────── +# Same contract used by agent-synthesized simulator files. + +RESIDUAL_RULES = [_wind_topples_start_block] + +PARAM_SPECS: List[ParamSpec] = [ + ParamSpec("wind_force", WIND_FORCE, lo=0.0, hi=1.0), + ParamSpec("wind_lever", WIND_LEVER, lo=0.0, hi=0.15), +] + +# Features the wind dynamics own. Scored by the rollout objective +# against observations; NOT overwritten at plan time - the engine moves +# them under the emitted force and torque. Roll is in scope because the +# effect being modelled is a topple, not a shove: a program that got the +# force right and the lever wrong slides the block instead of tipping +# it, and only roll separates the two. +RESIDUAL_FEATURES: Dict[str, List[str]] = { + "domino": ["x", "y", "roll"], +} + +# ── Factory binding ────────────────────────────────────────────── + + +class PyBulletDominoFanGroundTruthSimulatorFactory(GroundTruthSimulatorFactory + ): + """GT residual-dynamics simulator for pybullet_domino_fan. + + The simulator components (``RESIDUAL_RULES``, ``PARAM_SPECS``, + ``RESIDUAL_FEATURES``) live as module globals above; this class only + pins the env-name binding so ``get_gt_simulator`` can locate the + right module via the factory registry. + """ + + @classmethod + def get_env_names(cls) -> set: + return {"pybullet_domino_fan"} From 5bf261bdb626c8f43f816bccf6010937c9fba389 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 13:50:11 -0400 Subject: [PATCH 10/48] domino_fan config: the flags that make its tasks solvable The env block was written before domino_fan_aligned_tasks and domino_fan_wind_force existed, so it selected the env with neither: a chain pointed anywhere the generator liked, blown at the ball's 2.0 N. Both are the difference between a task and an impossible one, so they belong in the block rather than in a launcher someone has to remember. Turn ratios pinned to 0 as well - alignment only guarantees the FIRST leg runs downwind. Also records what the SKIP hides: this env has a GT residual simulator but no GT types, predicates, options or processes, so every arm raises NotImplementedError on the option lookup. Un-skipping it today buys a crash, and the comment says so rather than leaving the next person to find out. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/configs/predicatorv3/envs/all.yaml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index bbc13efc3..139e7a87b 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -340,6 +340,12 @@ ENVS: # task is a pure layout problem - arrange the chain so that switching the # fan on topples the target. The robot never pushes anything itself. # + # NOT RUNNABLE YET. pybullet_domino_fan has a GT residual simulator + # (ground_truth_models/domino_fan/) but no GT types, predicates, + # options or processes, so every arm - oracle included - raises + # NotImplementedError on the option lookup. Parked until those land; + # un-skipping it now only buys a crash. + # # NOTE: unlike the plain domino envs, these tasks carry NO DominoEvaluator # (the task generator attaches it only when dominoes are the sole dynamic # component). Reward is therefore plain goal-reached, with no per-blue @@ -362,6 +368,20 @@ ENVS: pybullet_birrt_path_subsample_ratio: 2 fan_known_controls_relation: True fan_fans_blow_opposite_direction: False + # Lay each chain along one fan's wind axis. Without this the + # generator points chains anywhere, and a chain across the wind + # cannot cascade however hard the fan blows - the task is + # unsolvable before a planner sees it. + domino_fan_aligned_tasks: True + # Calibrated for a domino, not the ball the FanComponent default + # (2.0 N) was set for: tipping threshold is 0.123 N, and past + # ~0.3 N the block stops toppling and starts flying. + domino_fan_wind_force: 0.2 + # Straight chains only. Alignment guarantees the FIRST leg runs + # downwind; a turn puts the rest of the chain across the wind, + # and the turn geometry was tuned for a robot push anyway. + domino_test_turn_ratio: 0.0 + domino_train_turn_ratio: 0.0 # coffee: # NAME: "pybullet_coffee" # FLAGS: From 1507924ab00d213345650b34b6175dae7c549923 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 14:10:52 -0400 Subject: [PATCH 11/48] domino_fan: GT models, so the oracle can run it pybullet_domino_fan now solves under oracle_process_planning: 1/1, reward 1.000, a 10-step plan. It was NotImplementedError before. Registered the env in the domino types, predicates, options and process factories, which needed almost nothing new - the composed env already publishes the fan component's types and predicates, and the grid helpers delegate to GridComponent either way. Two additions: * TurnFanOn / TurnFanOff options. A switch is pressed by pushing at its pose, so these are push skills, the same construction fan/options.py uses including its yaw correction (a push faces (sin yaw, cos yaw), a switch reports (cos rot, sin rot), so the conventions differ by a quarter turn and on/off are that quarter turn either side); * TurnFanOn and WindTopplesStartBlock processes. The second is the only bridging rule the wind needs: FanOn + InitialBlock + Upright -> Tilting, and the existing cascade rules carry it from there. Effects mirror PushStartBlock exactly, Tilting added and Upright deleted, because they are the same event reached two ways. Push is withheld in a fan env, option and process both. Left in, the planner takes it every time and solves a wind task without ever touching a switch - which is what the first run did, executing Pick/Place/Pick/Place/Push and never turning the fan on. Two things had to be written over FANS, not switches, and finding out why cost most of the debugging: under fan_known_controls_relation the FanComponent hides SwitchOn/SwitchOff and publishes FanOn/FanOff, so a guard keyed on "SwitchOn" in predicates never fired, no wind process was built, Push was already withheld, and the planner correctly reported that nothing could topple anything. The option takes the fan too, and resolves its switch by the side it controls. Also: Upright is an at-start condition on the wind, not an overall one. The process's own effect is the block ceasing to be upright, so requiring it throughout makes the rule contradict itself. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../ground_truth_models/domino/options.py | 99 +++++++++++++++- .../ground_truth_models/domino/predicates.py | 2 +- .../ground_truth_models/domino/processes.py | 108 ++++++++++++++++-- .../ground_truth_models/domino/types.py | 2 +- 4 files changed, 192 insertions(+), 19 deletions(-) diff --git a/predicators/ground_truth_models/domino/options.py b/predicators/ground_truth_models/domino/options.py index c1cb18d88..d2c5dd28b 100644 --- a/predicators/ground_truth_models/domino/options.py +++ b/predicators/ground_truth_models/domino/options.py @@ -4,8 +4,10 @@ from typing import ClassVar, Dict, Optional, Sequence, Set, Tuple from typing import Type as TypingType +import numpy as np from gym.spaces import Box +from predicators import utils from predicators.envs.pybullet_domino import PyBulletDominoEnv from predicators.envs.pybullet_env import PyBulletEnv from predicators.ground_truth_models import GroundTruthOptionFactory @@ -56,7 +58,7 @@ class PyBulletDominoGroundTruthOptionFactory(_DominoLegacyOptionsMixin, def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry" + "pybullet_domino_real_geometry", "pybullet_domino_fan" } @classmethod @@ -95,18 +97,103 @@ def _get_options_skill_factories( options: Set[ParameterizedOption] = set() - if CFG.domino_restricted_push: - options.add( - cls._create_sf_push_restricted(cfg, robot_type, domino_type)) - else: - options.add(cls._create_sf_push(cfg, robot_type, domino_type)) + # A fan env withholds Push on purpose. The wind is what starts + # the chain there, so leaving the robot a shove makes the fan + # decorative: the planner takes the cheaper Push every time and + # solves a wind task without ever touching a switch. Detected by + # the switch type, which only a FanComponent contributes. + if "switch" not in types: + if CFG.domino_restricted_push: + options.add( + cls._create_sf_push_restricted(cfg, robot_type, + domino_type)) + else: + options.add(cls._create_sf_push(cfg, robot_type, domino_type)) options.add(cls._create_sf_pick(cfg, robot_type, domino_type)) options.add(cls._create_sf_place(cfg, robot_type)) options.add(create_wait_option("Wait", cfg, robot_type)) + # A composed env carrying a FanComponent brings switches with + # it, and without a skill to press one the fan can never be + # turned on: every plan in pybullet_domino_fan starts there. + # Absent in the plain domino envs, whose types have no switch. + if "switch" in types: + options |= cls._create_sf_switch_options(cfg, robot_type, + types["switch"], + types.get("fan")) + return options + @classmethod + def _create_sf_switch_options( + cls, cfg: SkillConfig, robot_type: Type, switch_type: Type, + fan_type: Optional[Type]) -> Set[ParameterizedOption]: + """Press a switch on or off. + + A switch is pressed by pushing at its pose, so these are plain + push skills with the target taken from the switch - the same + construction ``fan/options.py`` uses, including its yaw + correction: a push skill faces (sin yaw, cos yaw) while a switch + reports its push direction as (cos rot, sin rot), so the two + conventions differ by a quarter turn, and on and off are that + quarter turn either side. + + Under ``fan_known_controls_relation`` the second argument is the + FAN, not the switch: the env hides SwitchOn/SwitchOff in that + mode and speaks only of FanOn/FanOff, so a process written over + fans needs an option it can share variables with. The switch is + then found from the fan, by the side it controls. + """ + known = CFG.fan_known_controls_relation and fan_type is not None + control_type = fan_type if known else switch_type + assert control_type is not None + option_types = [robot_type, control_type] + + def _switch_of(state: State, control: Object) -> Object: + if not known: + return control + switch = next( + (sw for sw in state.get_objects(switch_type) if state.get( + sw, "controls_fan") == state.get(control, "facing_side")), + None) + if switch is None: + raise utils.OptionExecutionFailure( + "No switch found for fan (controls_fan mismatch)") + return switch + + def _pose(state: State, objects: Sequence[Object], + sign: float) -> Tuple[float, float, float, float]: + _, control = objects + switch = _switch_of(state, control) + return (state.get(switch, + "x"), state.get(switch, + "y"), state.get(switch, "z"), + state.get(switch, "rot") + sign * np.pi / 2) + + def _on_pose(state: State, objects: Sequence[Object], params: Array, + config: SkillConfig) -> Tuple[float, float, float, float]: + del params, config + return _pose(state, objects, -1.0) + + def _off_pose( + state: State, objects: Sequence[Object], params: Array, + config: SkillConfig) -> Tuple[float, float, float, float]: + del params, config + return _pose(state, objects, +1.0) + + push_cfg = replace(cfg, transport_z=cls._transport_z_push) + return { + create_push_skill(name="TurnFanOn", + types=option_types, + config=push_cfg, + get_target_pose_fn=_on_pose), + create_push_skill(name="TurnFanOff", + types=option_types, + config=push_cfg, + get_target_pose_fn=_off_pose), + } + @classmethod def _build_skill_config( cls, diff --git a/predicators/ground_truth_models/domino/predicates.py b/predicators/ground_truth_models/domino/predicates.py index ede5294d2..f90b60665 100644 --- a/predicators/ground_truth_models/domino/predicates.py +++ b/predicators/ground_truth_models/domino/predicates.py @@ -19,7 +19,7 @@ class PyBulletDominoGroundTruthPredicateFactory(GroundTruthPredicateFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry" + "pybullet_domino_real_geometry", "pybullet_domino_fan" } @classmethod diff --git a/predicators/ground_truth_models/domino/processes.py b/predicators/ground_truth_models/domino/processes.py index 19e139f20..a9e99a8ac 100644 --- a/predicators/ground_truth_models/domino/processes.py +++ b/predicators/ground_truth_models/domino/processes.py @@ -167,7 +167,7 @@ class PyBulletDominoGroundTruthProcessFactory(GroundTruthProcessFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry" + "pybullet_domino_real_geometry", "pybullet_domino_fan" } @classmethod @@ -208,8 +208,9 @@ def get_processes( # We would need to add it to the environment for the DominoFall # exogenous process - # Options - Push = options["Push"] + # Options. Push is absent in a fan env (see below), so it is + # looked up defensively rather than by subscript. + Push = options.get("Push") Pick = options["Pick"] Place = options["Place"] Wait = options["Wait"] @@ -229,7 +230,6 @@ def get_processes( option_vars = [robot] else: option_vars = [robot, domino] - option = Push condition_at_start = { LiftedAtom(HandEmpty, [robot]), LiftedAtom(StartBlock, [domino]), @@ -244,12 +244,16 @@ def get_processes( ignore_effects = {DominoAtPos, DominoAtRot, PosClear, AdjacentTo} delay_distribution = DiscreteGaussianDelay(mu=torch.tensor(1.0), sigma=torch.tensor(0.1)) - push_start_block_process = EndogenousProcess( - "PushStartBlock", parameters, condition_at_start, set(), - set(), add_effects, delete_effects, delay_distribution, - torch.tensor(1.0), option, option_vars, _push_sampler, - ignore_effects) - processes.add(push_start_block_process) + if Push is not None: + push_start_block_process = EndogenousProcess( + "PushStartBlock", parameters, condition_at_start, set(), set(), + add_effects, delete_effects, delay_distribution, + torch.tensor(1.0), Push, option_vars, _push_sampler, + ignore_effects) + # Withheld in a fan env, matching the option: the wind starts + # the chain there, and a planner left a Push will use it and + # never touch a switch. + processes.add(push_start_block_process) # PickDomino: Position-based pick process robot = Variable("?robot", robot_type) @@ -394,6 +398,88 @@ def get_processes( delay_distribution, torch.tensor(1.0)) processes.add(domino_tilting_delete_process) + # --- Wind, when the env has fans ------------------------------- + # A composed env carrying a FanComponent brings switches and + # fans; a plain domino env does not, and its predicate dict has + # none of these names. + if "FanOn" in predicates: + processes |= cls._get_fan_processes(types, predicates, options) + + return processes + + @classmethod + def _get_fan_processes( + cls, types: Dict[str, Type], predicates: Dict[str, Predicate], + options: Dict[str, ParameterizedOption]) -> Set[CausalProcess]: + """Turning a fan on, and the wind that follows. + + Two processes are enough, and that is the point. The fan env + needs a grid because a ball's whole trajectory is wind; a domino + chain's is not. Only the FIRST block is pushed by the wind - + ``DominoFallFromBeingInFrontOfTilting`` and + ``DominoTiltingDelete`` above carry the cascade from there. So + the wind needs exactly one bridging rule into the vocabulary the + chain already speaks. + + Written over FANS rather than switches because that is the + vocabulary the env exposes: under + ``fan_known_controls_relation`` FanComponent hides + SwitchOn/SwitchOff and publishes FanOn/FanOff, and the switch is + an implementation detail the option resolves for itself. + """ + robot_type = types["robot"] + domino_type = types["domino"] + fan_type = types["fan"] + + FanOn = predicates["FanOn"] + FanOff = predicates["FanOff"] + Upright = predicates["Upright"] + StartBlock = predicates["InitialBlock"] + Tilting = predicates["Tilting"] + + processes: Set[CausalProcess] = set() + + # Pressing the switch. Endogenous: the robot does it. + robot = Variable("?robot", robot_type) + fan = Variable("?fan", fan_type) + processes.add( + EndogenousProcess( + "TurnFanOn", [robot, fan], {LiftedAtom(FanOff, [fan])}, set(), + set(), {LiftedAtom(FanOn, [fan])}, {LiftedAtom(FanOff, [fan])}, + DiscreteGaussianDelay(mu=torch.tensor(1.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["TurnFanOn"], [robot, fan], + null_sampler)) + + # The wind. Exogenous: nobody chooses it, it follows from the + # fan being on. + # + # Deliberately NOT conditioned on which way the fan faces, the + # way pybullet_fan's MoveToSide is. That needs a direction + # vocabulary the domino side has no use for, and the tasks are + # generated with the chain already laid along one fan's axis + # (domino_fan_aligned_tasks), so a fan press and a topple are + # one-to-one here. A task set where the planner had to CHOOSE a + # fan would need it, and this is where it would go. + # + # Effects mirror PushStartBlock exactly - Tilting added, Upright + # deleted - because the two are the same event reached two ways, + # and a rule that left Upright asserted could fire forever. + domino = Variable("?domino", domino_type) + fan2 = Variable("?fan", fan_type) + conds = { + LiftedAtom(FanOn, [fan2]), + LiftedAtom(StartBlock, [domino]), + LiftedAtom(Upright, [domino]), + } + processes.add( + ExogenousProcess( + "WindTopplesStartBlock", [domino, fan2], conds, set(), set(), + {LiftedAtom(Tilting, [domino])}, + {LiftedAtom(Upright, [domino])}, + DiscreteGaussianDelay(mu=torch.tensor(2.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0))) return processes @@ -639,7 +725,7 @@ class PyBulletDominoGroundTruthSamplerFactory(GroundTruthSamplerFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry" + "pybullet_domino_real_geometry", "pybullet_domino_fan" } @classmethod diff --git a/predicators/ground_truth_models/domino/types.py b/predicators/ground_truth_models/domino/types.py index a01524293..6e08b2bc9 100644 --- a/predicators/ground_truth_models/domino/types.py +++ b/predicators/ground_truth_models/domino/types.py @@ -19,7 +19,7 @@ class PyBulletDominoGroundTruthTypeFactory(GroundTruthTypeFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry" + "pybullet_domino_real_geometry", "pybullet_domino_fan" } @classmethod From c7c9fdbca503a51bb1a2401ebaaafe9bea0e73c3 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 26 Aug 2026 14:14:40 -0400 Subject: [PATCH 12/48] log_viewer: don't offer a reel for a run that recorded nothing Videos are only written under --make_test_videos / --make_interaction_videos, so plenty of runs have none - and every row got a watch button regardless, which spent the click to explain the emptiness afterwards. A run with no videos now shows "no video" in that column, and its name links to the logs instead of to a reel that could only say there is nothing here. The tooltip names the two flags that would have recorded them. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/log_viewer.py | 36 +++++++++++++++++++++++++++++++++--- 1 file changed, 33 insertions(+), 3 deletions(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 36411c71a..98fce324e 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1785,6 +1785,8 @@ def ansi_to_html(text: str) -> str: .fitvals { display: flex; flex-wrap: wrap; gap: 6px 18px; margin: 6px 0; } .fitvals code { color: var(--muted); } .fitvals b { font-family: var(--mono); color: var(--bright); } +.novid { font-size: 10px; color: var(--muted2); text-transform: uppercase; + letter-spacing: .08em; cursor: help; white-space: nowrap; } a.logslink { margin-left: 10px; font-size: 10px; color: var(--muted2); text-transform: uppercase; letter-spacing: .08em; white-space: nowrap; } tr.runrow:hover a.logslink, .topbar a.logslink:hover { @@ -2282,6 +2284,18 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, # that says which, and it says it reliably. kind = "oracle" if fam.startswith("oracle") else "learning" copy_path = os.path.relpath(os.path.join(LOGS_ROOT, r["rel"])) + # A run with no videos gets no watch link and its name points at the + # logs instead: the reel would only be able to say "nothing here". + has_vids = run_has_videos(r["rel"]) + if has_vids: + watch_cell = (f"" + "▶ watch") + primary = f"/replays?d={q(r['rel'])}" + else: + watch_cell = ("no video") + primary = f"/run?d={q(r['rel'])}" is_live = status == "running" esc_rel = esc(r["rel"]) kill_btn = "" @@ -2301,9 +2315,8 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, f"data-kind='{kind}' data-start='{start_ts:.0f}'>" f"" "" - f"" - "▶ watch" - f"{esc(r['name'])}" + f"{watch_cell}" + f"{esc(r['name'])}" f"logs" f"
" "
") @@ -2265,6 +2295,66 @@ def status_chip(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, return chip(label, cls, title) +# The domino-fan ladder, keyed by the arm that experiment_id carries. +# Each rung hands the agent less; the label says what it must supply, so +# a row reads as a rung of the experiment rather than an approach name. +_RUNG_BY_ARM = { + "domino_fan-oracle": (1, "GT sim + GT predicates, process planner plans"), + "domino_fan-agent_model_based_planning": + (2, "GT sim + GT predicates, the AGENT plans"), + "domino_fan-agent_param_learning": + (3, "GT sim structure; must fit the wind's parameters"), + "domino_fan-agent_po_predicate_invention_al": + (4, "base sim only; must find the wind, model it, invent predicates"), +} + + +def launch_rung(rung: str) -> Tuple[bool, str]: + """Start scripts/domino_fan/run_rung.sh for one rung, detached. + + Detached on purpose: the run outlives this request (rungs 3 and 4 + are hour-scale), and its output goes to the run's own log dir, which + the dashboard is already watching - so progress shows up as the rows + and episode chips it draws anyway, with no plumbing of its own. + + Refuses to start a second one. Two PyBullet runs at once is how a + laptop ends up with a dozen physics servers open, and the dashboard + would have no way to say which row belongs to which. + """ + if rung not in {"1", "2", "3", "4"}: + return False, f"unknown rung: {rung!r}" + # LOGS_ROOT is /logs; the script sits beside it. + repo_root = os.path.dirname(os.path.abspath(LOGS_ROOT)) + script = os.path.join(repo_root, "scripts", "domino_fan", "run_rung.sh") + if not os.path.isfile(script): + return False, f"missing {script}" + if _live_proc_matches(): + return False, ("a run is already going - stop it first with the " + "kill button on its row") + env = dict(os.environ, PYTHONHASHSEED="0") + with open(os.devnull, "wb") as devnull: + subprocess.Popen( # pylint: disable=consider-using-with + ["/bin/bash", script, rung], + cwd=repo_root, + stdout=devnull, + stderr=subprocess.STDOUT, + stdin=devnull, + start_new_session=True, + env=env) + return True, (f"rung {rung} started - its row appears once the run " + "creates its log dir (a few seconds)") + + +def rung_badge(exp: str) -> str: + """"RUNG n" chip for a domino-fan experiment, else empty.""" + key = exp.split("/")[-1] + entry = _RUNG_BY_ARM.get(key) + if entry is None: + return "" + num, what = entry + return f"RUNG {num}" + + def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, is_newest: bool) -> str: """One flat row on the index: what it was, how it went, where to look.""" @@ -2322,7 +2412,8 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, f"{del_btn}" f"{esc(fam)}" - f"{esc(expname)}" + f"{rung_badge(r['exp'])}" + f"{esc(expname)}" f"{esc(r['seed'])}" f"{status_chip(r, summary, live, is_newest)}{kill_btn}" f"{esc(tr_str)}" @@ -2383,6 +2474,22 @@ def index_page() -> str: body.append("") topbar = ("" + "" + "" + "run rung" + "" + "" + "" + "" + "" "" "" @@ -3212,7 +3319,9 @@ def do_POST(self) -> None: k: v[0] for k, v in urllib.parse.parse_qs(parsed.query).items() } - if parsed.path == "/kill": + if parsed.path == "/launch": + ok, msg = launch_rung(params.get("rung", "")) + elif parsed.path == "/kill": ok, msg = kill_run(params.get("d", "")) elif parsed.path == "/delete": ok, msg = delete_run(params.get("d", ""), From dfade9a45858e927240daba4690e2ee1f7779745 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 09:59:43 -0400 Subject: [PATCH 24/48] log_viewer: show a run's videos even when no transcript claims them An oracle run writes no agent transcripts - there is no LLM in the loop to write any - and the replay reel was built entirely from them, so it announced 'this run recorded no videos' with the mp4 sitting on disk beside it. The index row disagreed, offering a watch button that led to that page. Videos are now matched to the reel's rounds directly: whatever a transcript claims keeps its paired card, and the rest are shown with the env verdict read from info.log, which oracle runs do write. The reel's intro no longer promises 'watch it learn' on a run that has nothing to learn. reset_runs.sh takes a pattern, so the plain-domino scratch could be cleared without touching the friction system-ID result. --- scripts/domino_fan/reset_runs.sh | 38 +++++++---- scripts/log_viewer.py | 113 ++++++++++++++++++++++++++++--- 2 files changed, 131 insertions(+), 20 deletions(-) diff --git a/scripts/domino_fan/reset_runs.sh b/scripts/domino_fan/reset_runs.sh index b7d7cf101..3c8fc86a3 100755 --- a/scripts/domino_fan/reset_runs.sh +++ b/scripts/domino_fan/reset_runs.sh @@ -1,31 +1,45 @@ #!/usr/bin/env bash # Clear domino-fan runs so the dashboard shows only the ladder. # -# scripts/domino_fan/reset_runs.sh # what would be removed -# scripts/domino_fan/reset_runs.sh --yes # actually remove it +# scripts/domino_fan/reset_runs.sh # dry run +# scripts/domino_fan/reset_runs.sh --yes # remove them +# scripts/domino_fan/reset_runs.sh 'domino-oracle*' # another pattern +# scripts/domino_fan/reset_runs.sh 'domino-oracle*' --yes # -# Removes logs/, videos/, results/ and saved_approaches/ entries for -# every domino-fan experiment. The debugging runs from developing this -# env (domino-fan-v2, -w40, -corr, ...) clutter the dashboard and are -# not results; the ladder runs are cheap to regenerate with run_rung.sh. +# Removes logs/, videos/, results/ and saved_approaches/ entries whose +# experiment id matches the pattern (default: every domino-fan run). +# Ladder runs are cheap to regenerate with run_rung.sh, and the +# debugging runs from developing this env clutter the dashboard without +# being results. +# +# Careful with a wider pattern: a real experiment's run is the only copy +# of its transcripts and fits. The domino_high_friction_turn runs, for +# instance, are the friction system-ID result, not scratch. set -euo pipefail cd "$(dirname "$0")/../.." -DRY=1; [ "${1:-}" = "--yes" ] && DRY=0 +DRY=1 +PATTERN='domino*fan*' +for a in "$@"; do + case "$a" in + --yes) DRY=0 ;; + *) PATTERN="$a" ;; + esac +done targets=() while IFS= read -r d; do targets+=("$d"); done < <( - { ls -d logs/*/domino*fan* 2>/dev/null || true - ls -d videos/*/domino*fan* 2>/dev/null || true - ls results/*domino_fan* 2>/dev/null || true - ls saved_approaches/*domino_fan* 2>/dev/null || true + { ls -d logs/*/$PATTERN 2>/dev/null || true + ls -d videos/*/$PATTERN 2>/dev/null || true + ls results/*$PATTERN* 2>/dev/null || true + ls saved_approaches/*$PATTERN* 2>/dev/null || true } | sort -u) if [ ${#targets[@]} -eq 0 ]; then echo "nothing to remove."; exit 0; fi printf '%s\n' "${targets[@]}" echo "---" if [ "$DRY" = "1" ]; then - echo "${#targets[@]} paths. Re-run with --yes to remove them." + echo "${#targets[@]} paths match '$PATTERN'. Re-run with --yes to remove." else rm -rf "${targets[@]}" echo "removed ${#targets[@]} paths." diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index e5605d4f3..958368dcc 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -2636,13 +2636,20 @@ def replays_page(run_rel: str) -> Optional[str]: # reading as two attempts that never happened. by_round: Dict[int, List[str]] = {} seen: Set[str] = set() + claimed: Set[str] = set() for ep in summary["episodes"]: + names = episode_video_names(ep, run_rel) + claimed.update(n for n, _ in names) vids = episode_videos(ep, run_rel) if not vids or vids in seen: continue seen.add(vids) rnd = int(ep.get("round", 0)) by_round.setdefault(rnd, []).append(_clip_card(ep, vids, run_rel)) + # Videos no transcript owns still belong on the reel; an oracle run + # has nothing BUT those. + for rnd, cards in _orphan_clips(run_rel, summary, claimed).items(): + by_round.setdefault(rnd, []).extend(cards) fits_by_round: Dict[int, Dict[str, Any]] = { int(f["after_round"]): f for f in summary.get("fits", []) @@ -2663,12 +2670,20 @@ def replays_page(run_rel: str) -> Optional[str]: "written when main.py runs with --make_test_videos / " "--make_interaction_videos.

" ] + # An approach with no learning loop -- an oracle handed the true + # model -- has one round and nothing to learn between rounds, so the + # "watch it learn" reading would be a lie about what is on screen. + learns = bool(summary.get("fits")) or len(by_round) > 1 intro = ("") + "learn." if learns else + "") body = (f"
{intro}" f"{''.join(out)}
") @@ -2890,8 +2905,13 @@ def _video_figure(url: str, label: str) -> str: f"") -def episode_videos(ep: Dict[str, Any], run_rel: str) -> str: - """Players for the env episode this transcript belongs to. +def episode_video_names(ep: Dict[str, Any], + run_rel: str) -> List[Tuple[str, str]]: + """(filename, label) of the videos the env episode of this + transcript owns. + + Split out from the rendering so the replay reel can tell which of a + run's videos no transcript claims -- see _orphan_clips. main.py names a test video by task and learning cycle rather than by query, so the two transcripts of a replanned task (001/002 @@ -2905,9 +2925,8 @@ def episode_videos(ep: Dict[str, Any], run_rel: str) -> str: if ep["kind"] == "test" and ep["task"] is not None: key = (int(ep["task"]), _cycle_tag(int(ep.get("round", 0)))) for name, is_failure in videos_for_run(run_rel).get(key, []): - url = "/rawvideo?p=" + q(video_url_rel(run_rel, name)) label = "failure video" if is_failure else "test video" - out.append(_video_figure(url, label)) + out.append((name, label)) elif ep["kind"] == "explore": # Interaction videos are stamped with the 0-based learning cycle # directly (no cycleNone offset like test rounds), and an explore @@ -2921,9 +2940,87 @@ def episode_videos(ep: Dict[str, Any], run_rel: str) -> str: label = "interaction video" else: continue # another explore session's episode - url = "/rawvideo?p=" + q(video_url_rel(run_rel, name)) - out.append(_video_figure(url, label)) - return f"
{''.join(out)}
" if out else "" + out.append((name, label)) + return out + + +def episode_videos(ep: Dict[str, Any], run_rel: str) -> str: + """Players for the env episode this transcript belongs to.""" + figs = [ + _video_figure("/rawvideo?p=" + q(video_url_rel(run_rel, name)), label) + for name, label in episode_video_names(ep, run_rel) + ] + return f"
{''.join(figs)}
" if figs else "" + + +def _round_of_tag(tag: str, is_test: bool) -> int: + """The reel round a video's cycle tag belongs to. + + The inverse of _cycle_tag for test videos, whose round r>0 carries + the tag of learning cycle r-1. Interaction videos are stamped with + their cycle directly, so for those the tag IS the round. + """ + if tag in ("cycleNone", "cycle"): + return 0 + try: + cycle = int(tag[len("cycle"):]) + except ValueError: + return 0 + return cycle + 1 if is_test else cycle + + +def _orphan_clips(run_rel: str, summary: Dict[str, Any], + claimed: Set[str]) -> Dict[int, List[str]]: + """Cards for a run's videos that no transcript claims. + + An oracle run writes no agent transcripts at all -- there is no LLM + in the loop to write them -- so every one of its videos is an + orphan, and a reel built only from transcripts showed such a run as + "recorded no videos" while the mp4 sat on disk. The env verdict + still comes from info.log, which oracle runs do write. + """ + rounds = summary.get("rounds", []) + out: Dict[int, List[str]] = {} + for (task, tag), entries in sorted(videos_for_run(run_rel).items()): + rnd = _round_of_tag(tag, is_test=True) + for name, is_failure in entries: + if name in claimed: + continue + verdict = None + if rnd < len(rounds): + verdict = rounds[rnd].get(task) + what = f"test · task {task}" + label = "failure video" if is_failure else "test video" + out.setdefault(rnd, []).append( + _orphan_card(run_rel, name, label, what, verdict)) + for tag, ivs in sorted(interaction_videos_for_run(run_rel).items()): + rnd = _round_of_tag(tag, is_test=False) + for _, name in ivs: + if name in claimed: + continue + out.setdefault(rnd, []).append( + _orphan_card(run_rel, name, "interaction video", "practice", + None)) + return out + + +def _orphan_card(run_rel: str, name: str, label: str, what: str, + verdict: Optional[Dict[str, Any]]) -> str: + """One filmstrip card for a video with no transcript behind it.""" + url = "/rawvideo?p=" + q(video_url_rel(run_rel, name)) + vids = f"
{_video_figure(url, label)}
" + if verdict is None: + note = "no verdict" + else: + cls, word = ("ok", "solved") if verdict["solved"] else ("bad", + "failed") + reward = ("" if verdict.get("reward") is None else + f" {verdict['reward']:.2f}") + note = f"{word}{reward}" + return (f"
{vids}" + f"
{what} · {note}
" + "no transcript — this approach " + "writes none
") def episode_banner(ep: Dict[str, Any], n_rounds: int = 0) -> str: From a5444475f70d9d8f3f69979a151265fce164b996 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 10:17:12 -0400 Subject: [PATCH 25/48] domino_fan: ask for the fan, not for a push; and no dialogs in the viewer The generated goal text told every agent 'when the green domino is pushed ... only the green domino may ever be pushed' regardless of env. In a fan env that is an instruction the agent cannot follow and must not follow: it has no Push skill, and the certificate rejects any episode with no TurnFanOn on the record. The text now branches on the same trigger the evaluator is given, so the two cannot disagree, and the verification clause states what is actually checked for wind (a TurnFanOn step, nothing toppling before it) rather than a counterfactual replay that does not run for a switch press. The viewer's buttons used confirm()/alert(). One 'prevent this page from creating additional dialogs' checkbox -- Chrome offers it on any alert -- makes confirm() return false with no UI at all, so run rung, kill and delete silently did nothing with nowhere to read why. They go through an in-page ask bar and toast now, which cannot be suppressed. The index also opens with what is running: rung, elapsed, how far it has got, and a stop button, with the ladder buttons beside it. The first question this page gets asked was previously answerable only by finding the right row in a table sorted by something else. --- .../task_generators/domino_task_generator.py | 43 ++- .../task_generators/goal_text.py | 13 + scripts/configs/predicatorv3/_rung2.yaml | 21 ++ scripts/log_viewer.py | 254 ++++++++++++++---- 4 files changed, 271 insertions(+), 60 deletions(-) create mode 100644 scripts/configs/predicatorv3/_rung2.yaml diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index 39134ef93..38804836f 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -215,14 +215,35 @@ def _generate_single_task( target_word, target_verb = "the purple domino", "is" else: target_word, target_verb = "the purple dominoes", "are" - goal_nl = (f"Arrange the blue dominoes as needed (possibly none) such " - f"that when the green domino is pushed, {target_word} " - f"{target_verb} toppled. Only the blue dominoes may be " - f"rearranged: the green and purple dominoes must stay " - f"untouched at their staged poses, upright and never " - f"held, until the green is pushed, and nothing may " - f"topple before that push. Only the green domino may " - f"ever be pushed.") + # What starts the cascade, and so what the goal text must ask + # for. In a fan env the robot has no Push skill at all and the + # certificate demands a TurnFanOn step, so the push wording + # would be asking for something the agent cannot do and must + # not do -- an instruction it can only fail. + comp_names = {type(c).__name__ for c in self.additional_components} + fan_only = bool(comp_names) and comp_names <= {"FanComponent"} + trigger = "TurnFanOn" if fan_only else "Push" + if fan_only: + goal_nl = ( + f"Arrange the blue dominoes as needed (possibly none) such " + f"that when the fan is switched on, the wind topples the " + f"green domino and {target_word} {target_verb} toppled. " + f"Only the blue dominoes may be rearranged: the green and " + f"purple dominoes must stay untouched at their staged " + f"poses, upright and never held, until the fan is switched " + f"on, and nothing may topple before that. The robot must " + f"never push a domino - the only way to start the cascade " + f"is to press the fan's switch.") + else: + goal_nl = ( + f"Arrange the blue dominoes as needed (possibly none) such " + f"that when the green domino is pushed, {target_word} " + f"{target_verb} toppled. Only the blue dominoes may be " + f"rearranged: the green and purple dominoes must stay " + f"untouched at their staged poses, upright and never " + f"held, until the green is pushed, and nothing may " + f"topple before that push. Only the green domino may " + f"ever be pushed.") # Cascade-legitimacy evaluator (reward = certified success minus a # per-toppled-blue cost), same as the min-block tasks. Attached only @@ -238,9 +259,6 @@ def _generate_single_task( # rather than Push. A BALL is not - it is a second body the # robot can throw at the chain - so ball variants still get no # evaluator. - comp_names = {type(c).__name__ for c in self.additional_components} - fan_only = bool(comp_names) and comp_names <= {"FanComponent"} - trigger = "TurnFanOn" if fan_only else "Push" evaluator = None if CFG.domino_use_domino_blocks_as_target and \ (not self.additional_components or fan_only): @@ -267,7 +285,8 @@ def _generate_single_task( # Same reasoning for the legitimacy rule (see # goal_text.CASCADE_VERIFICATION_NL): an arm-assisted layout # otherwise fails with verdicts the agent cannot explain. - goal_nl += goal_text.CASCADE_VERIFICATION_NL + goal_nl += (goal_text.WIND_VERIFICATION_NL + if fan_only else goal_text.CASCADE_VERIFICATION_NL) return EnvironmentTask(init_state, goal_atoms, diff --git a/predicators/envs/pybullet_domino/task_generators/goal_text.py b/predicators/envs/pybullet_domino/task_generators/goal_text.py index 84c143747..69ecbd2c9 100644 --- a/predicators/envs/pybullet_domino/task_generators/goal_text.py +++ b/predicators/envs/pybullet_domino/task_generators/goal_text.py @@ -19,6 +19,19 @@ "fingertips made intangible, and the built layout must still cascade " "to the goal - topples that needed the arm's body earn nothing.") +# The wind variant of the same rule. There is no counterfactual replay +# to describe: the probe exists to prove the arm's body did not carry a +# cascade its push started, and a switch pressed metres from the chain +# has no such contact to disprove. What is enforced instead is that a +# TurnFanOn step is actually on the record -- otherwise an episode that +# reached the goal by knocking the chain over while placing a block +# would certify. +WIND_VERIFICATION_NL = ( + " A solve only counts if the wind starts the cascade: the episode " + "must carry a TurnFanOn step, nothing may topple before it, and an " + "episode that reaches the goal with no TurnFanOn on the record is " + "rejected.") + MIN_BLOCK_GOAL_NL = ( "Arrange the blue dominoes so that when the green domino is pushed, " "the purple domino is toppled -- using AS FEW blue dominoes as " diff --git a/scripts/configs/predicatorv3/_rung2.yaml b/scripts/configs/predicatorv3/_rung2.yaml new file mode 100644 index 000000000..829c0daa6 --- /dev/null +++ b/scripts/configs/predicatorv3/_rung2.yaml @@ -0,0 +1,21 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 2. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_fan: + SKIP: False +APPROACHES: + oracle: + SKIP: True + agent_model_based_planning: + SKIP: False + FLAGS: + num_online_learning_cycles: 0 + agent_param_learning: + SKIP: True + agent_po_predicate_invention_al: + SKIP: True diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 958368dcc..86f491ecc 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1805,10 +1805,32 @@ def ansi_to_html(text: str) -> str: a.watchlink:hover { text-decoration: none; filter: brightness(1.1); } .muted { color: var(--muted); font-weight: 400; } -.launchbar { align-items: center; } -.launchlbl { padding: 0 8px; font-size: 9px; font-weight: 700; - letter-spacing: .1em; text-transform: uppercase; color: var(--muted); - border-right: 1px solid var(--border2); cursor: help; } +/* The "what is happening right now" panel above the run table. */ +.livepanel { border: 1px solid var(--border); border-radius: 8px; + background: var(--panel); padding: 12px 14px; margin: 0 0 14px; } +.livehead { display: flex; gap: 10px; align-items: baseline; + margin-bottom: 2px; } +.liverow { display: flex; gap: 12px; align-items: center; flex-wrap: wrap; + padding: 8px 0; border-top: 1px solid var(--border); margin-top: 8px; } +.liverow .prog { color: var(--muted); font-family: var(--mono); + font-size: 11px; } +/* A pulse, because a still dot beside "running" is exactly what a + stalled page also shows. */ +.liverow .dot { width: 8px; height: 8px; border-radius: 50%; + background: var(--ok); flex-shrink: 0; animation: pulse 1.6s infinite; } +@keyframes pulse { 50% { opacity: .25; } } +.launchrow { display: flex; gap: 8px; align-items: center; + flex-wrap: wrap; margin-top: 10px; } +button.rungbtn { height: 26px; padding: 0 12px; font-weight: 600; } +button.rungbtn[disabled] { opacity: .4; cursor: not-allowed; } +/* Armed: one more click starts it. Warn-coloured, because the click + after this one spends real time and real tokens. */ +button.rungbtn.armed { border-color: var(--warn); color: var(--warn); + background: var(--warn-dim); } +.lmsg { font-size: 11px; color: var(--muted); } +.lmsg.warn { color: var(--warn); } +.lmsg.ok { color: var(--ok); } +.lmsg.bad { color: var(--bad); } .kindbar { display: inline-flex; border: 1px solid var(--border2); border-radius: 5px; overflow: hidden; flex-shrink: 0; } .kindbar button.kindbtn { height: 24px; border: none; border-radius: 0; @@ -1918,17 +1940,69 @@ def ansi_to_html(text: str) -> str: } // Index page: filter + compare + collapsible groups. +// No native dialogs anywhere in this page. +// This used to use confirm()/alert(). A browser that has been told to +// block a page's dialogs -- one checkbox on any earlier alert, and +// Chrome offers it -- makes confirm() return false with no UI at all, +// so buttons silently did nothing and there was nowhere to read why. +// ask() and toast() are ordinary DOM, so they cannot be suppressed. +function toast(text, cls) { + var t = $('#toast'); + if (!t) { + t = document.createElement('div'); + t.id = 'toast'; + document.body.appendChild(t); + } + t.className = cls || ''; + t.textContent = text; + t.hidden = false; + clearTimeout(window._toastTimer); + window._toastTimer = setTimeout(function() { t.hidden = true; }, 8000); +} +function askOpen() { return !!$('#askbar'); } +function closeAsk() { + var b = $('#askbar'); + if (b) b.remove(); +} +function ask(msg, yesLabel, onYes) { + closeAsk(); + var bar = document.createElement('div'); + bar.id = 'askbar'; + var t = document.createElement('span'); + t.className = 'askmsg'; + t.textContent = msg; + var yes = document.createElement('button'); + yes.className = 'askyes'; + yes.textContent = yesLabel; + yes.onclick = function() { closeAsk(); onYes(); }; + var no = document.createElement('button'); + no.textContent = 'cancel'; + no.onclick = closeAsk; + bar.appendChild(t); + bar.appendChild(yes); + bar.appendChild(no); + document.body.appendChild(bar); + yes.focus(); +} +function setLaunchMsg(cls, text) { + var m = $('#launchmsg'); + if (!m) return; + m.className = 'lmsg ' + cls; + m.textContent = text; +} function launchRung(n) { - if (!confirm('Start rung ' + n + '?\n\nRung 1 takes ~2 minutes. ' - + 'Rungs 2-4 call Claude and can run for an hour or more; ' - + 'stop one with the kill button on its row.')) return; - fetch('/launch?rung=' + n, {method: 'POST'}).then(function(r) { - return r.text().then(function(t) { - alert(t); - // The row appears once the run makes its log dir. - if (r.ok) setTimeout(function() { location.reload(); }, 6000); - }); - }).catch(function(e) { alert('launch failed: ' + e); }); + var btn = $('button.rungbtn[data-rung="' + n + '"]'); + var what = btn ? btn.dataset.warn : ''; + ask('Start rung ' + n + '? ' + what, 'start rung ' + n, function() { + setLaunchMsg('', 'starting rung ' + n + '\u2026'); + fetch('/launch?rung=' + n, {method: 'POST'}).then(function(r) { + return r.text().then(function(t) { + setLaunchMsg(r.ok ? 'ok' : 'bad', t); + // The row appears once the run makes its log dir. + if (r.ok) setTimeout(function() { location.reload(); }, 6000); + }); + }).catch(function(e) { setLaunchMsg('bad', 'launch failed: ' + e); }); + }); } function kindFilter() { return sessionStorage.getItem('lv-kind') || 'all'; } function setKind(k) { @@ -1984,33 +2058,37 @@ def ansi_to_html(text: str) -> str: } function compareSelected() { var sel = $all('input.cmp:checked').map(function(c) { return c.value; }); - if (sel.length < 2) { alert('Select at least two runs to compare.'); return; } + if (sel.length < 2) { + toast('Tick at least two runs to compare them.', 'warn'); + return; + } location.href = '/compare?runs=' + encodeURIComponent(sel.join(';')); } // Kill / delete buttons on index run rows. POST only, so the auto- // refresh GETs can never trip these; reload shortly after success so // the status chip reflects the process actually exiting. -function postRun(url, msg) { - if (!confirm(msg)) return; - fetch(url, {method: 'POST'}).then(function(r) { - r.text().then(function(t) { - if (!r.ok) { alert(t); return; } - setTimeout(function() { location.reload(); }, 600); - }); - }).catch(function(e) { alert('request failed: ' + e); }); +function postRun(url, msg, yesLabel) { + ask(msg, yesLabel, function() { + fetch(url, {method: 'POST'}).then(function(r) { + r.text().then(function(t) { + if (!r.ok) { toast(t, 'bad'); return; } + setTimeout(function() { location.reload(); }, 600); + }); + }).catch(function(e) { toast('request failed: ' + e, 'bad'); }); + }); } function killRun(rel) { postRun('/kill?d=' + encodeURIComponent(rel), - 'Kill the live process of\\n' + rel + ' ?'); + 'Stop the running process of ' + rel + '?', 'stop it'); } function deleteRun(rel, live) { var msg = live - ? 'This run appears LIVE:\\n' + rel + - '\\nKill its process AND delete its log dir (and videos)?' - : 'Delete the log dir (and videos) of\\n' + rel + ' ?'; + ? 'This run is LIVE: ' + rel + + ' - stop its process AND delete its log dir and videos?' + : 'Delete the log dir and videos of ' + rel + '?'; postRun('/delete?d=' + encodeURIComponent(rel) + (live ? '&kill=1' : ''), - msg); + msg, live ? 'stop and delete' : 'delete'); } // Run page: hash routing into the content pane. @@ -2123,7 +2201,10 @@ def ansi_to_html(text: str) -> str: if (window._stamp === undefined) { window._stamp = s; return; } if (s !== window._stamp) { window._stamp = s; - if (autoOn()) { location.reload(); } else { showRefreshPill(); } + // Never reload with a question on screen: the reader would lose + // the click they were about to make. + if (autoOn() && !askOpen()) { location.reload(); } + else { showRefreshPill(); } } }).catch(function() {}); } @@ -2192,8 +2273,8 @@ def page(title: str, topbar_extra: str, body: str) -> str: "process planner plans. 2: same, but the AGENT plans. 3: the " "simulator's structure only, its parameters fitted from data. 4: " "the base simulator alone - it must find the wind, model it, and " - "invent predicates. Hover a badge for that rung's line. Start one " - "with run rung in the top bar." + "invent predicates. Hover a badge for that rung's line. Start " + "one with the buttons at the top of this page." "
approach
" "
The method. An oracle_* approach is handed the " "ground-truth model and learns nothing - it is an upper bound. An " @@ -2345,6 +2426,98 @@ def launch_rung(rung: str) -> Tuple[bool, str]: "creates its log dir (a few seconds)") +# Each rung's one-line warning, shown when its button is armed. The +# cost of a click is the thing a reader most needs before making it. +_RUNG_BLURB = { + "1": "Oracle: ground-truth simulator and predicates, the process " + "planner plans. ~2 minutes, no LLM calls.", + "2": "Ground-truth simulator and predicates, but the AGENT plans. " + "Calls Claude; usually a few minutes.", + "3": "The simulator's structure only - the agent must fit the wind " + "parameters from data. Calls Claude; can run for an hour.", + "4": "Base simulator alone - find the wind, model it, invent " + "predicates. Calls Claude; the longest rung, hours.", +} + + +def _live_progress(summary: Dict[str, Any]) -> str: + """What a running run has got through so far, in a few words.""" + eps = summary.get("episodes", []) + n_test = sum(1 for e in eps if e["kind"] == "test") + n_exp = sum(1 for e in eps if e["kind"] == "explore") + n_fit = len(summary.get("fits", [])) + bits = [] + if n_exp: + bits.append(f"{n_exp} practice") + if n_test: + bits.append(f"{n_test} test") + if n_fit: + bits.append(f"{n_fit} fit" + ("s" if n_fit != 1 else "")) + # No episode has been logged yet: the run is in setup (importing + # pybullet, building the env), which takes tens of seconds. + return " \u00b7 ".join(bits) if bits else "starting up" + + +def live_panel(runs: List[Dict[str, Any]], summaries: Dict[str, Dict[str, + Any]], + live: LiveProcs, newest: Dict[Tuple[str, str], + Tuple[float, str]]) -> str: + """What is running right now, and the buttons to start something. + + The first question anyone brings to this page is "is it running?", + and it used to be answerable only by finding the right row in a + table sorted by something else. So the live runs come first, with + their elapsed time and how far they have got, and the ladder + buttons sit beside them - where the answer to "can I start one?" + is visible at the moment of asking. + """ + now = time.time() + cards = [] + for r in runs: + summary = summaries.get(r["rel"], {}) + is_newest = newest.get((r["exp"], r["seed"]), (0.0, ""))[1] == r["rel"] + status, _ = run_status(r, summary, live, is_newest) + if status != "running": + continue + start_ts = _run_start_ts(r["name"], r["mtime"]) + _, _, expname = r["exp"].partition("/") + expname = expname or r["exp"] + esc_rel = esc(r["rel"]) + cards.append( + f"
" + f"{rung_badge(r['exp'])}{esc(expname)}" + f"{esc(r['seed'])}" + f"running {_fmt_duration(now - start_ts)}" + f"" + f"{_live_progress(summary)}" + f"logs" + f"watch" + f"
") + if cards: + head = ("
Running now" + "updates on its own; the run keeps " + "going if you close this page
") + state = head + "".join(cards) + # The server refuses a second run anyway (two PyBullet servers at + # once is how the machine ends up thrashing); saying so on the + # buttons beats saying it in an error after the click. + btn_note = " disabled title='Stop the running one first'" + else: + state = ("
Nothing running" + "start a rung of the domino-fan " + "ladder below
") + btn_note = "" + btns = "".join( + f"" + for n in ("1", "2", "3", "4")) + return ("
" + state + + f"
{btns}" + "
") + + def rung_badge(exp: str) -> str: """"RUNG n" chip for a domino-fan experiment, else empty.""" key = exp.split("/")[-1] @@ -2445,7 +2618,8 @@ def index_page() -> str: newest[key] = (ts, r["rel"]) body = [ "
", LEGEND_HTML + "overflow:auto'>", + live_panel(runs, summaries, live, newest), LEGEND_HTML ] if not runs: body.append( @@ -2474,22 +2648,6 @@ def index_page() -> str: body.append("
") topbar = ("" - "" - "" - "run rung" - "" - "" - "" - "" - "" "" "" From ea4b4c5e0d0b9d910ec9721bed0ac3ab0bdbab70 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 10:19:33 -0400 Subject: [PATCH 26/48] domino_fan: one place decides the trigger, so no builder can contradict it The push/wind choice now comes from _wind_triggered(), used by the goal text, the verification clause, and the evaluator's sanctioned trigger alike. The min-block builder also picks between them: nothing pairs domino_min_block_tasks with a fan today, but the flags are independent and the push wording is unfollowable in a fan env, so the trap is disarmed rather than left set. Checked: pybullet_domino still reads push + replay-probe, pybullet_domino_fan reads fan + wind verification. --- .../task_generators/domino_task_generator.py | 27 +++++++++++++------ .../task_generators/goal_text.py | 14 ++++++++++ 2 files changed, 33 insertions(+), 8 deletions(-) diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index 38804836f..b62757a07 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -105,6 +105,21 @@ def generate_tasks( return tasks + def _wind_triggered(self) -> bool: + """True when the cascade is started by wind, not by a push. + + A fan is the only additional component that can start a cascade + legitimately without the arm touching a domino, so it decides + both the trigger the certificate sanctions and what the goal + text may ask for. Those two must never disagree: an agent told + to push the green in a fan env has no Push skill to push with, + and every episode it runs is rejected for having no TurnFanOn + on the record. A ball is a second body the robot can throw at + the chain, so ball variants are not wind-triggered. + """ + comp_names = {type(c).__name__ for c in self.additional_components} + return bool(comp_names) and comp_names <= {"FanComponent"} + def _generate_single_task( self, task_idx: int, @@ -215,13 +230,7 @@ def _generate_single_task( target_word, target_verb = "the purple domino", "is" else: target_word, target_verb = "the purple dominoes", "are" - # What starts the cascade, and so what the goal text must ask - # for. In a fan env the robot has no Push skill at all and the - # certificate demands a TurnFanOn step, so the push wording - # would be asking for something the agent cannot do and must - # not do -- an instruction it can only fail. - comp_names = {type(c).__name__ for c in self.additional_components} - fan_only = bool(comp_names) and comp_names <= {"FanComponent"} + fan_only = self._wind_triggered() trigger = "TurnFanOn" if fan_only else "Push" if fan_only: goal_nl = ( @@ -376,7 +385,9 @@ def _generate_min_block_task( goal_atoms.add(GroundAtom(self.domino.Toppled, [domino_obj])) return EnvironmentTask(init_state, goal_atoms, - goal_nl=goal_text.MIN_BLOCK_GOAL_NL) + goal_nl=(goal_text.MIN_BLOCK_WIND_GOAL_NL + if self._wind_triggered() else + goal_text.MIN_BLOCK_GOAL_NL)) # A chain's travel direction is (sin rotation, cos rotation) -- see # _place_straight_domino -- so rotation is measured from +y, turning diff --git a/predicators/envs/pybullet_domino/task_generators/goal_text.py b/predicators/envs/pybullet_domino/task_generators/goal_text.py index 69ecbd2c9..0806c727b 100644 --- a/predicators/envs/pybullet_domino/task_generators/goal_text.py +++ b/predicators/envs/pybullet_domino/task_generators/goal_text.py @@ -41,6 +41,20 @@ "may topple before that push. Only the green domino may ever be " "pushed." + CASCADE_VERIFICATION_NL) +# The min-block instruction for a wind-triggered env. Nothing pairs +# domino_min_block_tasks with a fan today, but the two flags are +# independent and the push wording is unfollowable in a fan env, so the +# builder picks between them rather than leaving a trap set. +MIN_BLOCK_WIND_GOAL_NL = ( + "Arrange the blue dominoes so that when the fan is switched on, the " + "wind topples the green domino and the purple domino is toppled -- " + "using AS FEW blue dominoes as possible (possibly none). Only the " + "blue dominoes may be rearranged: the green and purple dominoes must " + "stay untouched at their staged poses, upright and never held, until " + "the fan is switched on, and nothing may topple before that. The " + "robot must never push a domino - the only way to start the cascade " + "is to press the fan's switch." + WIND_VERIFICATION_NL) + HEAVY_GOAL_NL = ( "Arrange the blue dominoes so that when the green domino is pushed, " "the purple domino is toppled -- using AS FEW blue dominoes as " From 6a86e2f340c1580066b0f6dce9d84eecd8a6793f Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 10:23:31 -0400 Subject: [PATCH 27/48] log_viewer: name the compare column, and say why a reel is empty Three unlabeled things on this page each made a reader guess. The checkbox column had no header, so the only way to learn what ticking a row does was to tick one and hunt for the button. It is called cmp now, with the tooltip on the header, and the button says 'Compare 2 runs' and stays dead until it can do anything. 'This run recorded no videos ... pass --make_test_videos' was told to every empty reel, including runs that were still going and runs that had been killed - sending a reader to fix a config that was already right. The three causes now read differently. 'This approach does not learn' was decided by counting fits, so a learner killed in its first round got the oracle's line: the interruption described as if it were the method. It reads the approach family instead, and an agent arm with no online learning cycles gets its own line rather than either. Rung 2 on the corrected goal text: 1/1, reward 0.950 - a one-block solve, where the oracle spends two. --- scripts/log_viewer.py | 111 +++++++++++++++++++++++++++++++++--------- 1 file changed, 88 insertions(+), 23 deletions(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 86f491ecc..d61e7cb85 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1805,6 +1805,9 @@ def ansi_to_html(text: str) -> str: a.watchlink:hover { text-decoration: none; filter: brightness(1.1); } .muted { color: var(--muted); font-weight: 400; } +/* The compare column: an unlabeled checkbox is a question, not a + control. Small and grey, but named. */ +th.cmphdr { font-size: 9px; letter-spacing: .08em; cursor: help; } /* The "what is happening right now" panel above the run table. */ .livepanel { border: 1px solid var(--border); border-radius: 8px; background: var(--panel); padding: 12px 14px; margin: 0 0 14px; } @@ -2056,6 +2059,15 @@ def ansi_to_html(text: str) -> str: rows.forEach(function(r) { tbody.appendChild(r); }); }); } +function paintCompare() { + var n = $all('input.cmp:checked').length; + var b = $('#cmpbtn'); + if (!b) return; + b.disabled = n < 2; + b.textContent = n ? 'Compare ' + n + ' runs' : 'Compare'; + b.title = n < 2 ? 'Tick two or more runs in the cmp column' + : 'Line these runs up side by side'; +} function compareSelected() { var sel = $all('input.cmp:checked').map(function(c) { return c.value; }); if (sel.length < 2) { @@ -2212,6 +2224,7 @@ def ansi_to_html(text: str) -> str: paintAutoBtn(); paintThemeBtn(); paintKindBtns(); + paintCompare(); paintSortBtn(); applySort(); pollStamp(); @@ -2576,7 +2589,9 @@ def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, return ("" - f"" + f"" "" f"{watch_cell}" f"{esc(r['name'])}" @@ -2626,7 +2641,10 @@ def index_page() -> str: f"

No run_* directories found under {esc(LOGS_ROOT)}.

") else: head = ( - "" + "cmp" "" "run" "" - "{esc(LOGS_ROOT)}") + "" + f"{esc(LOGS_ROOT)}") return page("runs - log viewer", topbar, "".join(body)) @@ -2775,6 +2795,67 @@ def _fit_card(fit: Dict[str, Any]) -> str: "the planner for the next round.") +def _is_oracle(run_rel: str) -> bool: + """True for an oracle_* approach: handed ground truth, learns nothing. + + Read off the approach family in the path rather than off how much + the run happened to log. A learner killed in its first round has no + fits and one round too, and calling that "does not learn" describes + the interruption as if it were the method. + """ + return run_rel.split("/")[0].startswith("oracle") + + +def _reel_intro(run_rel: str, summary: Dict[str, Any]) -> str: + """The one line above the reel, saying what kind of run this is.""" + if _is_oracle(run_rel): + return ("This approach does not learn: it is handed the " + "ground-truth model and solves the held-out tasks once. " + "What follows is that test.") + if not summary.get("fits"): + # An agent arm with no online learning cycles: it still has to + # plan and act, it just never refits a model mid-run. + return ("The agent plans and acts here with the model it was " + "given - this run has no online learning cycles, so " + "there is no fit between rounds. What follows is its " + "test.") + return ("The whole run, oldest first. Each round is one turn of the " + "loop: the robot practices to gather data, the fit " + "recovers what the world is really like, and a test " + "on a held-out task scores it. Read top to bottom to watch " + "it learn.") + + +def _no_videos_reason(run_rel: str, summary: Dict[str, Any]) -> str: + """Why an empty reel is empty. + + "No videos" has three quite different causes and only one of them + is about flags: a run still going has not written any yet, and a + killed one never will. Saying the flag line to all three sends a + reader to change a config that was already correct. + """ + if not summary.get("done"): + runs = find_runs() + live = live_runs(runs) + r = next((x for x in runs if x["rel"] == run_rel), None) + if r is not None: + newest = max( + (y for y in runs + if (y["exp"], y["seed"]) == (r["exp"], r["seed"])), + key=lambda y: _run_start_ts(y["name"], y["mtime"])) + status, _ = run_status(r, summary, live, + newest["rel"] == run_rel) + if status == "running": + return ("This run is still going. Clips appear here as " + "its episodes finish - the page refreshes " + "itself.") + return ("This run was stopped before it recorded a video. " + "Its transcripts and logs are still there under " + "logs & transcripts.") + return ("This run recorded no videos. They are written when main.py " + "runs with --make_test_videos / --make_interaction_videos.") + + def replays_page(run_rel: str) -> Optional[str]: """A run's whole pipeline as a reel: practice, what it learned, test. @@ -2823,25 +2904,9 @@ def replays_page(run_rel: str) -> Optional[str]: out.append(f"

Round {rnd + 1}

" f"{strip}{band}
") if not out: - out = [ - "

This run recorded no videos. They are " - "written when main.py runs with --make_test_videos / " - "--make_interaction_videos.

" - ] - # An approach with no learning loop -- an oracle handed the true - # model -- has one round and nothing to learn between rounds, so the - # "watch it learn" reading would be a lie about what is on screen. - learns = bool(summary.get("fits")) or len(by_round) > 1 - intro = ("" if learns else - "") + out = [f"

{_no_videos_reason(run_rel, summary)}

"] + intro = f"" body = (f"
{intro}" f"{''.join(out)}
") From fc4db1c1c0ff8d50062e498744fcd2f5b63c8582 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 10:25:19 -0400 Subject: [PATCH 28/48] log_viewer: the confirm bar had no CSS, so it opened off-screen I added ask() and never styled it. Appended to , which sits behind a content div of height 100vh - topbar, the bar rendered below the fold: created correctly, invisible, and clicking a rung button looked exactly like the confirm() bug it replaced. It is fixed-positioned now, above everything else on the page. The click also writes into the panel's own status line, which is inside the scrolling content and cannot go off-screen the same way - a control I cannot click-test here should leave more than one trace. --- scripts/log_viewer.py | 31 ++++++++++++++++++++++++++++++- 1 file changed, 30 insertions(+), 1 deletion(-) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index d61e7cb85..1e52a1b46 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1830,6 +1830,27 @@ def ansi_to_html(text: str) -> str: after this one spends real time and real tokens. */ button.rungbtn.armed { border-color: var(--warn); color: var(--warn); background: var(--warn-dim); } +/* The confirm bar and the toast. Both are appended to , which + sits behind a content div of height 100vh - topbar: without fixed + positioning they render below the fold and a click looks like it did + nothing at all. */ +#askbar { position: fixed; left: 50%; transform: translateX(-50%); + bottom: 22px; z-index: 200; display: flex; gap: 10px; + align-items: center; max-width: min(760px, 92vw); + padding: 12px 14px; border: 1px solid var(--warn); + border-radius: 8px; background: var(--panel); + box-shadow: 0 8px 30px rgba(0, 0, 0, .35); } +#askbar .askmsg { font-size: 12px; line-height: 1.4; } +#askbar button { height: 28px; padding: 0 12px; flex-shrink: 0; } +#askbar button.askyes { border-color: var(--warn); color: var(--warn); + background: var(--warn-dim); font-weight: 600; } +#toast { position: fixed; left: 50%; transform: translateX(-50%); + bottom: 22px; z-index: 200; max-width: min(760px, 92vw); + padding: 10px 14px; border: 1px solid var(--border2); + border-radius: 8px; background: var(--panel); font-size: 12px; + box-shadow: 0 8px 30px rgba(0, 0, 0, .35); } +#toast.bad { border-color: var(--bad); color: var(--bad); } +#toast.warn { border-color: var(--warn); color: var(--warn); } .lmsg { font-size: 11px; color: var(--muted); } .lmsg.warn { color: var(--warn); } .lmsg.ok { color: var(--ok); } @@ -1980,7 +2001,10 @@ def ansi_to_html(text: str) -> str: yes.onclick = function() { closeAsk(); onYes(); }; var no = document.createElement('button'); no.textContent = 'cancel'; - no.onclick = closeAsk; + no.onclick = function() { + closeAsk(); + setLaunchMsg('', ''); + }; bar.appendChild(t); bar.appendChild(yes); bar.appendChild(no); @@ -1996,6 +2020,11 @@ def ansi_to_html(text: str) -> str: function launchRung(n) { var btn = $('button.rungbtn[data-rung="' + n + '"]'); var what = btn ? btn.dataset.warn : ''; + // Also say it in the panel, which is inside the scrolling content and + // certain to be on screen. The ask bar is fixed-positioned and the + // click has to leave SOME visible trace even if that ever breaks. + setLaunchMsg('warn', 'Confirm at the bottom of the page to start rung ' + + n + '.'); ask('Start rung ' + n + '? ' + what, 'start rung ' + n, function() { setLaunchMsg('', 'starting rung ' + n + '\u2026'); fetch('/launch?rung=' + n, {method: 'POST'}).then(function(r) { From 76a3edd29aa6bbb7a439c7b5384a74a586fe3a22 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Sat, 29 Aug 2026 10:59:54 -0400 Subject: [PATCH 29/48] domino_fan: no PyBullet GUI clients in a run nobody is watching approaches/all.yaml sets option_model_use_gui: True on agent_param_learning, so rung 3 opened a real p.GUI client per option-model env - and the run rebuilds that env repeatedly. Ten 1024x796 windows were open in the one process before it was half done, which is what 'so many pybullet open' was: not many processes, one process with many windows. Rungs 1, 2 and 4 have the flag off, which is why it only showed up on rung 3. The generated rung config forces it off. Watching a rollout is worth a window when a person is watching; a run started from a script or a dashboard button has nobody there. I had twice reported this as 'one process, headless' from the process list alone. The process list cannot see windows - Quartz's CGWindowList can, and did. --- scripts/domino_fan/run_rung.sh | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index c416d5d0c..299e78663 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -58,13 +58,27 @@ lines = [ ] for a in arms: lines += [f" {a}:", f" SKIP: {a != arm}"] - if a == arm and a == "agent_model_based_planning": + if a != arm: + continue + flags = [ + # approaches/all.yaml sets option_model_use_gui: True on + # agent_param_learning (and on agent_sim_learning and + # agent_oracle_mono_sim). That opens a REAL PyBullet GUI client + # per option-model env, and this ladder rebuilds that env many + # times over a run: rung 3 had ten 1024x796 windows open in one + # process before it was half done. Useful when a person is + # watching a rollout, pure cost for a batch run started from a + # script or a dashboard button, where nobody is. + " option_model_use_gui: False", + ] + if a == "agent_model_based_planning": # Nothing to learn with a GT model, so online cycles only # re-derive a solved plan at ~$2/cycle. Matches what # agent_oracle_hybrid_sim already does. - lines += [" FLAGS:", " num_online_learning_cycles: 0"] - elif a == arm and a.startswith("agent_p"): - lines += [" FLAGS:", " skip_initial_test: True"] + flags.append(" num_online_learning_cycles: 0") + elif a.startswith("agent_p"): + flags.append(" skip_initial_test: True") + lines += [" FLAGS:"] + flags print("\n".join(lines)) PYEOF From 8eb5dfb44b011eaf5eef8edcf97162cd9fb892e9 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 08:56:27 -0400 Subject: [PATCH 30/48] domino_fan: the fit could not reach the answer PARAM_SPECS bounded wind_force to [0, 1.0] while the env blows at CFG.domino_fan_wind_force = 1.5. The true value sat outside the search box, so the rung was unwinnable by construction: run_20260829_110429 spent three online cycles creeping 0.2251 -> 0.2229 (-1% per cycle) and scored 0/1 with reward -0.05 every time. Mine to own: I wrote the spec with the env at 0.2, then calibrated the wind to 1.5 for the topple onset and never revisited the bound. The comment still claimed the constant was 'equal to the env's domino_fan_wind_force', which it had not been for days. The bound now derives from the env's own setting with room either side, so recalibrating the wind cannot strand the fit again. The init stays a deliberately wrong 0.2 - recovering the value from rollouts is the point of the rung. --- .../domino_fan/gt_simulator.py | 21 +++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/predicators/ground_truth_models/domino_fan/gt_simulator.py b/predicators/ground_truth_models/domino_fan/gt_simulator.py index bfb7884d5..97045f26d 100644 --- a/predicators/ground_truth_models/domino_fan/gt_simulator.py +++ b/predicators/ground_truth_models/domino_fan/gt_simulator.py @@ -58,10 +58,12 @@ # ── Constants ──────────────────────────────────────────────────── -# Held-mode force (N), equal to the env's domino_fan_wind_force. A 100 g +# Starting guess for the wind force (N). NOT the true value: the env +# blows at CFG.domino_fan_wind_force, and recovering that number from +# rollouts is the whole job of the parameter-learning rung. A 100 g # domino 15 mm thick and 150 mm tall, pushed at 0.4 of its height, tips -# at m*g*(depth/2)/(0.4*height) = 0.123 N; this sits ~60% over that, the -# same margin the ball's 0.06 N keeps over its stiction. Fitted init. +# at m*g*(depth/2)/(0.4*height) = 0.123 N, so this init clears tipping +# with the same margin the ball's 0.06 N keeps over its stiction. WIND_FORCE = 0.2 # Height above the domino's origin the wind effectively acts at (m), @@ -157,8 +159,19 @@ def _wind_topples_start_block(state: State, updates: ResidualUpdate, RESIDUAL_RULES = [_wind_topples_start_block] +# The search box the fit is allowed to look in. It MUST contain the +# env's true values or the rung is unwinnable by construction: hi=1.0 +# excluded the env's 1.5 N, and run_20260829_110429 spent three cycles +# creeping 0.2251 -> 0.2229 and scoring 0/1 every time, because the +# answer was outside the box. The bound is now set from the env's own +# setting with room either side, so recalibrating the wind cannot +# silently strand the fit again. +_FORCE_HI = max(3.0, 2.0 * CFG.domino_fan_wind_force) + PARAM_SPECS: List[ParamSpec] = [ - ParamSpec("wind_force", WIND_FORCE, lo=0.0, hi=1.0), + ParamSpec("wind_force", WIND_FORCE, lo=0.0, hi=_FORCE_HI), + # 0.4 * domino_height = 0.06 for the default 0.15 m domino, so the + # true lever sits mid-box here. ParamSpec("wind_lever", WIND_LEVER, lo=0.0, hi=0.15), ] From e5b62ab3c8ddb0d55aad8b0453556ae1f56db85f Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 09:20:10 -0400 Subject: [PATCH 31/48] domino_fan: skip param learning, learn the wind rule instead Group decision: the just-fit-the-parameters rung is not the interesting setting for this env, so rung 4 - where the agent has to discover and write the wind rule itself - is the milestone. Rung 3 still runs, but says why it is not meaningful here first: the wind acts for ~2 steps before the start block tips, so the whole observation is 'tipped or did not', and 1.5 N and 2.0 N produce identical trajectories. That is a property of a domino, not of the optimizer - pybullet_fan can fit the same parameter because a ball's entire trajectory is wind. Also drops agent_sim_provide_base_sim_source from exp_domino_fan.yaml. It was a no-op that read like a feature: the ball env can turn it on because it split its sim core into pybullet_fan_base.py with no wind law in it, and this env has no such split - the wind sits in fan_component.py beside the fan bodies - so the flag only produced 'declares no base-sim source files; providing none'. The successful friction run had it off too. --- scripts/configs/predicatorv3/_rung3.yaml | 22 +++++++++++++++++++ .../configs/predicatorv3/exp_domino_fan.yaml | 15 ++++++++----- scripts/domino_fan/run_rung.sh | 8 ++++++- scripts/log_viewer.py | 10 +++++---- 4 files changed, 45 insertions(+), 10 deletions(-) create mode 100644 scripts/configs/predicatorv3/_rung3.yaml diff --git a/scripts/configs/predicatorv3/_rung3.yaml b/scripts/configs/predicatorv3/_rung3.yaml new file mode 100644 index 000000000..1ddd7c2e2 --- /dev/null +++ b/scripts/configs/predicatorv3/_rung3.yaml @@ -0,0 +1,22 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 3. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_fan: + SKIP: False +APPROACHES: + oracle: + SKIP: True + agent_model_based_planning: + SKIP: True + agent_param_learning: + SKIP: False + FLAGS: + option_model_use_gui: False + skip_initial_test: True + agent_po_predicate_invention_al: + SKIP: True diff --git a/scripts/configs/predicatorv3/exp_domino_fan.yaml b/scripts/configs/predicatorv3/exp_domino_fan.yaml index 5c74c9b4b..3d29932ba 100644 --- a/scripts/configs/predicatorv3/exp_domino_fan.yaml +++ b/scripts/configs/predicatorv3/exp_domino_fan.yaml @@ -67,8 +67,13 @@ APPROACHES: SKIP: True FLAGS: skip_initial_test: True - # Hands over the scene and the skills the way exp_fan.yaml does - # for the ball task. The WIND dynamics are not in that source - - # they live in the residual program the agent must rediscover - - # so this is not handing over the answer. - agent_sim_provide_base_sim_source: True + # NOT enabled: this flag is a no-op for this env and reads as if + # it were not. exp_fan.yaml can turn it on because the ball env + # split its sim core into pybullet_fan_base.py, a module holding + # geometry and switch mechanics and deliberately NO wind law. + # pybullet_domino_fan has no such split - the wind lives inside + # fan_component.py next to the fan bodies - so + # get_base_sim_source_files() returns [] and the run logs + # "declares no base-sim source files; providing none". + # Turning it on would need that module split first. + # agent_sim_provide_base_sim_source: True diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index 299e78663..226c018bc 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -29,7 +29,13 @@ run_one() { case "$rung" in 1) arm="oracle" ; exp="rung1_oracle" ;; 2) arm="agent_model_based_planning" ; exp="rung2_agent_planner" ;; - 3) arm="agent_param_learning" ; exp="rung3_param_learning" ;; + 3) arm="agent_param_learning" ; exp="rung3_param_learning" + echo "NOTE: rung 3 fits the wind's parameters, and in this env they" >&2 + echo " are not identifiable: the wind acts for ~2 steps before" >&2 + echo " the start block tips, so the whole observation is 'tipped" >&2 + echo " or did not'. 1.5 N and 2.0 N give identical trajectories." >&2 + echo " Rung 4 learns the wind RULE instead, which is the open" >&2 + echo " question here. Running anyway." >&2 ;; 4) arm="agent_po_predicate_invention_al" ; exp="rung4_full_learning" ;; *) echo "unknown rung: $rung" >&2; usage ;; esac diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index 1e52a1b46..a7147345f 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -2313,10 +2313,12 @@ def page(title: str, topbar_extra: str, body: str) -> str: "
Where a domino-fan run sits on the ladder - each rung hands " "the agent less. 1: ground-truth simulator and predicates, the " "process planner plans. 2: same, but the AGENT plans. 3: the " - "simulator's structure only, its parameters fitted from data. 4: " - "the base simulator alone - it must find the wind, model it, and " - "invent predicates. Hover a badge for that rung's line. Start " - "one with the buttons at the top of this page.
" + "simulator's structure only, its parameters fitted from data - " + "SKIPPED on domino-fan, where the wind acts for ~2 steps and its " + "force is not identifiable. 4: the base simulator alone - it must " + "find the wind, model it, and invent predicates; this is the open " + "question here. Hover a badge for that rung's line. Start one " + "with the buttons at the top of this page." "
approach
" "
The method. An oracle_* approach is handed the " "ground-truth model and learns nothing - it is an upper bound. An " From 73cc014370d67697e9939696f7c2d2e09e84f965 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 09:38:37 -0400 Subject: [PATCH 32/48] domino_fan: keep the wind-identifiability finding, drop the runs probe_wind_identifiability.py measures, in about a minute and with no LLM, whether a change in the wind force produces a change a fitter could see. It does not: below ~0.8 N nothing moves at all (static friction), above it the block tips in ~2 steps, and 1.5 N and 2.0 N give identical trajectories. Post-topple drift is non-monotonic, so it is tumbling rather than signal. That was the one thing worth keeping from the interrupted rung 3 runs, and it is better as a script than as a log directory: it re-answers the question after any change to the wind, the block, or the friction, instead of standing as a fact that was true once. The four interrupted runs are deleted. What is left is a clean ladder: rung 1 at 0.900, rung 2 at 0.950, rung 4 running. --- scripts/configs/predicatorv3/_rung4.yaml | 22 ++++ .../probe_wind_identifiability.py | 119 ++++++++++++++++++ 2 files changed, 141 insertions(+) create mode 100644 scripts/configs/predicatorv3/_rung4.yaml create mode 100644 scripts/domino_debug/probe_wind_identifiability.py diff --git a/scripts/configs/predicatorv3/_rung4.yaml b/scripts/configs/predicatorv3/_rung4.yaml new file mode 100644 index 000000000..a764ba4ac --- /dev/null +++ b/scripts/configs/predicatorv3/_rung4.yaml @@ -0,0 +1,22 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 4. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_fan: + SKIP: False +APPROACHES: + oracle: + SKIP: True + agent_model_based_planning: + SKIP: True + agent_param_learning: + SKIP: True + agent_po_predicate_invention_al: + SKIP: False + FLAGS: + option_model_use_gui: False + skip_initial_test: True diff --git a/scripts/domino_debug/probe_wind_identifiability.py b/scripts/domino_debug/probe_wind_identifiability.py new file mode 100644 index 000000000..e6b5007f4 --- /dev/null +++ b/scripts/domino_debug/probe_wind_identifiability.py @@ -0,0 +1,119 @@ +"""Is the domino-fan wind force recoverable from rollouts? Measure it. + +Answers, in about a minute and with no LLM in the loop, the question +that otherwise costs a 40-minute learning run to answer badly: does a +change in ``domino_fan_wind_force`` produce a change a fitter could +see? + +It does not, and the reason is the domino rather than the optimizer. +The wind acts only while the start block is upright (a fallen domino +is out of the airstream), which at any force that topples at all is +about two simulation steps. So the entire observation is "tipped or +did not", and the map from force to that observation is a step: + + force (N) topple step drift (mm) + 0.10 never 0.02 + 0.25 never 0.00 + 0.50 never 0.01 + 0.75 never 0.03 + 1.00 5 7.70 + 1.50 2 9.49 <- the env's true value + 2.00 2 19.23 + 3.00 1 11.16 + +1.5 N and 2.0 N are indistinguishable. Below ~0.8 N nothing moves at +all -- static friction holds the block, so there is no gentle +sub-threshold signal either -- and the post-topple drift is +non-monotonic, being tumbling rather than a function of the parameter. +A run's own sysID diagnostics said the same thing in their own words +("weakly identified", "NOT identified (posterior ~= prior)"). + +Contrast ``pybullet_fan``, which fits this same parameter happily: its +wind pushes a BALL with no stopping condition, so every timestep's +position is a continuous monotone function of the force. Whether a +wind parameter is fittable is a property of what the wind is pushing. + +Run it after any change to the wind, the block geometry, or the +friction, to check whether the answer has moved: + + PYTHONHASHSEED=0 python scripts/domino_debug/probe_wind_identifiability.py +""" + +import numpy as np + +from predicators import utils + + +def main() -> None: + """Sweep the wind force; report what an observer could measure.""" + utils.reset_config({ + "env": "pybullet_domino_fan", + "seed": 0, + "num_test_tasks": 1, + "num_train_tasks": 0, + "domino_fan_aligned_tasks": True, + }) + # Imported after reset_config so the env sees the right settings. + # pylint: disable=import-outside-toplevel + from predicators.envs.pybullet_domino.components.domino_component import \ + DominoComponent + from predicators.envs.pybullet_domino.env import PyBulletDominoFanEnv + from predicators.settings import CFG + from predicators.structs import Action, EnvironmentTask + + env = PyBulletDominoFanEnv(use_gui=False) + base = env.get_test_tasks()[0] + thresh = DominoComponent.domino_roll_threshold + + # The same scene, but with every fan already blowing at t=0: the + # robot is held still, so the wind is the only thing acting and + # nothing else can be credited with the topple. + init = base.init.copy() + for obj in init: + if obj.type.name in ("fan", "switch"): + init.set(obj, "is_on", 1.0) + green = next(o for o in init + if o.type.name == "domino" and all( + abs(float(init.get(o, c)) - + DominoComponent.start_domino_color[i]) < 1e-3 + for i, c in enumerate(("r", "g", "b")))) + task = EnvironmentTask(init, base.goal) + + def drift(state) -> float: + """How far the block has moved from where it started (m).""" + return float( + np.hypot(state.get(green, "x") - init.get(green, "x"), + state.get(green, "y") - init.get(green, "y"))) + + def probe(force: float): + """(steps until the block topples, drift) at this wind force.""" + CFG.domino_fan_wind_force = force + env._current_task = task # pylint: disable=protected-access + env._set_state(init) # pylint: disable=protected-access + # Hold the arm exactly where it is. + hold = np.array(env._pybullet_robot.get_joints(), # pylint: disable=protected-access + dtype=np.float32) + state = init + for step in range(150): + env.step(Action(hold)) + state = env._get_state() # pylint: disable=protected-access + roll = float(state.get(green, "roll")) + # Roll is meaningful modulo pi: a box turned 180 degrees + # about its width axis is the same box. + roll = (roll + np.pi / 2) % np.pi - np.pi / 2 + if abs(roll) >= thresh: + return step, drift(state) + return None, drift(state) + + print(f"\n{'force (N)':>10} {'topple step':>13} {'drift (mm)':>12}") + for force in (0.10, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00, 3.00): + step, dist = probe(force) + mark = " <- env's value" if abs(force - 1.5) < 1e-9 else "" + print(f"{force:>10.2f} {str(step):>13} {1000 * dist:>12.2f}{mark}", + flush=True) + print("\nDistinct topple-step values across a 30x range of force is the " + "\nwhole signal a fitter has. See the module docstring.\n") + + +if __name__ == "__main__": + main() From 496cb6acdd19d55dbae5c20333ccbaee5a1d4064 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 10:26:13 -0400 Subject: [PATCH 33/48] domino_declare: the robot declares finished instead of pressing a button A new env, pybullet_domino_declare: same scene, same wind, same certificate as pybullet_domino_fan, with the switch removed. The robot runs DeclareFinished and the fan starts. Two reasons it earns its own env. A physical button is awkward on a real arm - it must be reachable from every staging pose, the approach must not sweep through the chain just built, and a missed press looks exactly like a press that did not take. And for a learning arm the declaration is the cleaner question: nothing is touched, so there is no contact to credit the wind to, and an agent that finds the relation has found a causal one rather than a mechanical one. How it works. Action.extra_info carries a DECLARE_FINISHED_KEY flag (the field existed and had no users); _step_once stashes the action so _domain_specific_step, which takes no arguments, can read it. The switch body still stores the on/off bit - a fan's is_on is read off its joint - but it is parked two metres outside the workspace, so a press is not merely unnecessary here, it is impossible. The process model says it plainly: DeclareFinished(?robot) has effect FanOn(?fan). An option that does not take the fan as an argument, and an effect on it anyway. Verified: the env builds, the latch turns the fan on and off, the robot is offered DeclareFinished and not TurnFanOn, the goal text and the certificate both name DeclareFinished as the sanctioned trigger, and the wind's residual dynamics are shared with the button env. run_rung.sh --declare N runs the same ladder on it. --- .../pybullet_domino/cascade_certificate.py | 9 +- .../components/fan_component.py | 36 +++++- predicators/envs/pybullet_domino/env.py | 94 +++++++++++++-- .../task_generators/domino_task_generator.py | 31 ++++- .../task_generators/goal_text.py | 10 ++ predicators/envs/pybullet_env.py | 10 ++ .../ground_truth_models/domino/options.py | 27 +++-- .../ground_truth_models/domino/predicates.py | 3 +- .../ground_truth_models/domino/processes.py | 59 +++++++-- .../ground_truth_models/domino/types.py | 3 +- .../domino_fan/gt_simulator.py | 8 +- .../skill_factories/declare.py | 114 ++++++++++++++++++ predicators/structs.py | 9 ++ scripts/configs/predicatorv3/envs/all.yaml | 43 +++++++ scripts/domino_fan/run_rung.sh | 27 +++-- 15 files changed, 440 insertions(+), 43 deletions(-) create mode 100644 predicators/ground_truth_models/skill_factories/declare.py diff --git a/predicators/envs/pybullet_domino/cascade_certificate.py b/predicators/envs/pybullet_domino/cascade_certificate.py index 6e7a3d46a..22e4c8c52 100644 --- a/predicators/envs/pybullet_domino/cascade_certificate.py +++ b/predicators/envs/pybullet_domino/cascade_certificate.py @@ -80,7 +80,14 @@ # switch metres away has no body contact to disprove. Rules (a)-(c) # still apply and are what reject the arm-knocked-the-target episodes # this env was scoring as wins. -_BODYLESS_TRIGGERS = frozenset({_WIND_TRIGGER_OPTION_NAME}) +_DECLARE_TRIGGER_OPTION_NAME = "DeclareFinished" + +# Triggers that start a cascade without the arm touching anything, and +# so have no contact for the counterfactual probe to disprove. The +# switch press is one because the switch is metres from the chain; a +# declaration is one because it moves nothing at all. +_BODYLESS_TRIGGERS = frozenset( + {_WIND_TRIGGER_OPTION_NAME, _DECLARE_TRIGGER_OPTION_NAME}) # Consecutive non-held states a domino must spend at or past # ``fallen_threshold`` before that counts as a topple rather than as diff --git a/predicators/envs/pybullet_domino/components/fan_component.py b/predicators/envs/pybullet_domino/components/fan_component.py index e7c1852f3..22f09640b 100644 --- a/predicators/envs/pybullet_domino/components/fan_component.py +++ b/predicators/envs/pybullet_domino/components/fan_component.py @@ -68,7 +68,8 @@ def __init__(self, table_width: float = 1.0, num_sides: int = 4, fans_per_side: Optional[int] = None, - switch_xy: Optional[Tuple[float, float]] = None) -> None: + switch_xy: Optional[Tuple[float, float]] = None, + switch_reachable: bool = True) -> None: """Initialize the fan component. Args: @@ -85,6 +86,13 @@ def __init__(self, fans_per_side: Fan bodies in each side's bank (default keeps the class values). They blow as one; only fan_ids[0] is read for direction or wind, so a bank is decoration. + switch_reachable: When False, the switch bodies are parked + far outside the workspace. The switch stays the thing + that STORES whether the fan is on -- extract_feature + reads the fan's is_on off its joint -- but nothing can + reach it, so the only way the fan comes on is whatever + the env decides to latch it with (see + PyBulletDominoDeclareEnv, where a declaration does). switch_xy: Where the first switch sits, overriding the workspace-centre formula below. That formula assumes the fan env's workspace, which is twice as deep with the @@ -98,6 +106,7 @@ def __init__(self, super().__init__() assert 1 <= num_sides <= 4, num_sides self.num_sides = num_sides + self.switch_reachable = switch_reachable if fans_per_side is not None: self.num_left_fans = fans_per_side self.num_right_fans = fans_per_side @@ -144,6 +153,14 @@ def __init__(self, else: self.switch_base_x, self.switch_y = switch_xy self.switch_x_spacing = 0.08 + if not switch_reachable: + # Parked well outside the arm's reach and off the table. + # The body still exists and still stores the on/off bit, + # but no skill and no stray sweep of the arm can touch it, + # so the fan's state has exactly one cause: whatever the + # env latches it with. + self.switch_base_x = self.x_lb - 2.0 + self.switch_y = self.y_lb - 2.0 # Side names. A component built with fewer sides keeps the # first N of these: side 0 (left) blows +x, and the domino-fan @@ -390,6 +407,23 @@ def step(self) -> None: # Fan-specific methods # ------------------------------------------------------------------------- + def set_fans_on(self, on: bool = True) -> None: + """Turn every fan on or off without anything pressing anything. + + The switch joint IS the stored bit -- ``extract_feature`` reads + a fan's ``is_on`` off the switch that controls its side -- so + setting the fan means setting that joint. Written for envs + where the fan has no reachable button and something else + decides (see PyBulletDominoDeclareEnv). In the button envs + nothing calls this and the press remains the only cause. + """ + for switch in self._switches: + self._set_switch_on(switch.id, on) + + def any_fan_on(self) -> bool: + """True when at least one fan is currently blowing.""" + return any(self._is_switch_on(sw.id) for sw in self._switches) + def set_wind_target(self, target_id: int, z_offset: float = 0.0, diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index 2b0340565..71266e305 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -34,8 +34,9 @@ from predicators.pybullet_helpers.objects import create_object from predicators.pybullet_helpers.robots import SingleArmPyBulletRobot from predicators.settings import CFG -from predicators.structs import Action, EnvironmentTask, GroundAtom, Object, \ - ParameterizedOption, Predicate, State, TaskEvaluator, Type +from predicators.structs import DECLARE_FINISHED_KEY, Action, \ + EnvironmentTask, GroundAtom, Object, ParameterizedOption, Predicate, \ + State, TaskEvaluator, Type class DominoEvaluator(TaskEvaluator): @@ -1052,21 +1053,98 @@ def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: # domino chain runs one way, and the aligned generator lays it # along side 0. The other three fans would be distractors the # planner still has to ground, and being opposed they cancel. - fan_comp = FanComponent(workspace_bounds=bounds, - table_height=self.table_height, - table_width=self.table_width, - num_sides=CFG.domino_fan_num_sides, - fans_per_side=1, - switch_xy=DOMINO_FAN_SWITCH_XY) + fan_comp = self._make_fan_component(bounds) super().__init__(components=[domino_comp, fan_comp], use_gui=use_gui, **kwargs) + def _make_fan_component(self, bounds: Dict[str, float]) -> FanComponent: + """The fan bank for this env. Overridden where the switch is + not something the robot can reach.""" + return FanComponent(workspace_bounds=bounds, + table_height=self.table_height, + table_width=self.table_width, + num_sides=CFG.domino_fan_num_sides, + fans_per_side=1, + switch_xy=DOMINO_FAN_SWITCH_XY) + @classmethod def get_name(cls) -> str: return "pybullet_domino_fan" +class PyBulletDominoDeclareEnv(PyBulletDominoFanEnv): + """The fan env with the button removed: the robot DECLARES finished. + + Same scene and same physics as ``pybullet_domino_fan`` -- arrange + the blues so the wind carries a cascade from the green start block + to the purple target -- with one thing changed: there is no switch + to press. The robot runs a ``DeclareFinished`` skill, and the fan + comes on. + + Two reasons the change is worth its own env rather than a flag. + + **It is the version that survives contact with real hardware.** A + physical button has to be reachable from every staging pose, the + arm has to approach it without sweeping through the chain it just + built, and a missed press looks exactly like a press that did not + take. A declaration has none of that. + + **It makes the causal question clean.** With a button, "what starts + the wind" has a mechanical answer an agent can stumble into: the + gripper touched a thing. Here nothing is touched. An agent that + works out that the wind follows its declaration has found a + relation that is causal and nothing else -- which is precisely what + the predicate-invention rung is meant to be testing. + + The switch body still exists and still stores the bit (a fan's + ``is_on`` is read off its joint), but it is parked two metres + outside the workspace, so a press is not merely unnecessary here: + it is impossible. + """ + + def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: + super().__init__(use_gui=use_gui, **kwargs) + # Latched by the declaration, cleared on reset. Kept on the env + # rather than read from the switch each step because the + # question it answers is "has the robot declared yet", which is + # about the episode, not about the bodies. + self._declared: bool = False + + @classmethod + def get_name(cls) -> str: + return "pybullet_domino_declare" + + def _make_fan_component(self, bounds: Dict[str, float]) -> FanComponent: + """A fan whose switch nothing can reach.""" + return FanComponent(workspace_bounds=bounds, + table_height=self.table_height, + table_width=self.table_width, + num_sides=CFG.domino_fan_num_sides, + fans_per_side=1, + switch_xy=DOMINO_FAN_SWITCH_XY, + switch_reachable=False) + + def _set_domain_specific_state(self, state: State) -> None: + super()._set_domain_specific_state(state) + # A reset is a new episode: the declaration does not carry over, + # or the second task of a run would begin with the wind already + # blowing on a chain nobody has built yet. + self._declared = bool(self._fan_component is not None + and self._fan_component.any_fan_on()) + + def _domain_specific_step(self) -> None: + # The declaration, before the wind: the flag it sets is what + # makes any wind happen at all this step. + action = self._last_action + info = getattr(action, "extra_info", None) + if isinstance(info, dict) and info.get(DECLARE_FINISHED_KEY): + self._declared = True + if self._fan_component is not None: + self._fan_component.set_fans_on(True) + super()._domain_specific_step() + + class PyBulletDominoFanRampEnv(PyBulletDominoComposedEnv): """Domino + fan + ball + ramp environment class.""" diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index b62757a07..74cb946f3 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -231,8 +231,27 @@ def _generate_single_task( else: target_word, target_verb = "the purple dominoes", "are" fan_only = self._wind_triggered() - trigger = "TurnFanOn" if fan_only else "Push" - if fan_only: + # Which skill is sanctioned to start the cascade. The two fan + # envs differ only here: one presses a button, one declares. + if not fan_only: + trigger = "Push" + elif CFG.env == "pybullet_domino_declare": + trigger = "DeclareFinished" + else: + trigger = "TurnFanOn" + if fan_only and trigger == "DeclareFinished": + goal_nl = ( + f"Arrange the blue dominoes as needed (possibly none) such " + f"that once you declare you have finished building, the " + f"wind topples the green domino and {target_word} " + f"{target_verb} toppled. Only the blue dominoes may be " + f"rearranged: the green and purple dominoes must stay " + f"untouched at their staged poses, upright and never held, " + f"until you declare finished, and nothing may topple " + f"before that. There is no switch to press and the robot " + f"must never push a domino - declaring finished is the " + f"only thing that starts the cascade.") + elif fan_only: goal_nl = ( f"Arrange the blue dominoes as needed (possibly none) such " f"that when the fan is switched on, the wind topples the " @@ -294,8 +313,12 @@ def _generate_single_task( # Same reasoning for the legitimacy rule (see # goal_text.CASCADE_VERIFICATION_NL): an arm-assisted layout # otherwise fails with verdicts the agent cannot explain. - goal_nl += (goal_text.WIND_VERIFICATION_NL - if fan_only else goal_text.CASCADE_VERIFICATION_NL) + if not fan_only: + goal_nl += goal_text.CASCADE_VERIFICATION_NL + elif trigger == "DeclareFinished": + goal_nl += goal_text.DECLARE_VERIFICATION_NL + else: + goal_nl += goal_text.WIND_VERIFICATION_NL return EnvironmentTask(init_state, goal_atoms, diff --git a/predicators/envs/pybullet_domino/task_generators/goal_text.py b/predicators/envs/pybullet_domino/task_generators/goal_text.py index 0806c727b..5de7ffab8 100644 --- a/predicators/envs/pybullet_domino/task_generators/goal_text.py +++ b/predicators/envs/pybullet_domino/task_generators/goal_text.py @@ -41,6 +41,16 @@ "may topple before that push. Only the green domino may ever be " "pushed." + CASCADE_VERIFICATION_NL) +# The declaration variant. Same rule, different named step: there is +# no counterfactual replay for a skill that moves nothing, so what is +# checked is that the declaration is on the record and that nothing +# fell before it. +DECLARE_VERIFICATION_NL = ( + " A solve only counts if the wind starts the cascade: the episode " + "must carry a DeclareFinished step, nothing may topple before it, " + "and an episode that reaches the goal with no DeclareFinished on " + "the record is rejected.") + # The min-block instruction for a wind-triggered env. Nothing pairs # domino_min_block_tasks with a fan today, but the two flags are # independent and the push wording is unfollowable in a fan env, so the diff --git a/predicators/envs/pybullet_env.py b/predicators/envs/pybullet_env.py index 24d83a624..6e51fe66b 100644 --- a/predicators/envs/pybullet_env.py +++ b/predicators/envs/pybullet_env.py @@ -302,6 +302,11 @@ def __init__(self, # Used by sim-learning to create base-sim-only envs. self._skip_domain_specific_dynamics: bool = skip_residual_dynamics + # The action currently being stepped, for domain dynamics that + # depend on what the skill announced rather than on where the + # arm went (see Action.extra_info). + self._last_action: Optional[Action] = None + # Drives real hardware from this env's rollouts; None means pure sim, # which is what every env built by the planner stays. self._executor: Optional[ActionExecutor] = None @@ -727,6 +732,11 @@ def _step_once(self, action: Action, render_obs: bool = False) -> Observation: """Advance the simulation one action, with no executor involved.""" + # Stashed so _domain_specific_step, which takes no arguments, + # can still read what the action carried. Only envs with a + # signalling skill look at it (see Action.extra_info); the + # rest never touch it. + self._last_action = action self._step_base(action) if not self._skip_domain_specific_dynamics: self._domain_specific_step() diff --git a/predicators/ground_truth_models/domino/options.py b/predicators/ground_truth_models/domino/options.py index d81f5293c..5ea730b41 100644 --- a/predicators/ground_truth_models/domino/options.py +++ b/predicators/ground_truth_models/domino/options.py @@ -14,6 +14,8 @@ from predicators.ground_truth_models.skill_factories import SkillConfig, \ create_pick_skill, create_place_skill, create_push_skill, \ create_wait_option, shared_skill_robot, shared_skill_simulator +from predicators.ground_truth_models.skill_factories.declare import \ + create_declare_option from predicators.ground_truth_models.skill_factories.pick import _PICK_PARAMS from predicators.pybullet_helpers.robots import SingleArmPyBulletRobot from predicators.settings import CFG @@ -58,7 +60,8 @@ class PyBulletDominoGroundTruthOptionFactory(_DominoLegacyOptionsMixin, def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry", "pybullet_domino_fan" + "pybullet_domino_real_geometry", "pybullet_domino_fan", + "pybullet_domino_declare" } @classmethod @@ -115,13 +118,23 @@ def _get_options_skill_factories( options.add(create_wait_option("Wait", cfg, robot_type)) # A composed env carrying a FanComponent brings switches with - # it, and without a skill to press one the fan can never be - # turned on: every plan in pybullet_domino_fan starts there. - # Absent in the plain domino envs, whose types have no switch. + # it, and without a skill to start the fan it can never be + # turned on: every plan in a fan env starts there. Absent in + # the plain domino envs, whose types have no switch. + # + # HOW the fan starts is the difference between the two fan + # envs. pybullet_domino_fan gives the robot a button and a push + # skill to press it. pybullet_domino_declare parks the switch + # outside the workspace and the robot instead DECLARES it has + # finished building - no contact, nothing to reach around, and + # for a learner nothing mechanical to credit the wind to. if "switch" in types: - options |= cls._create_sf_switch_options(cfg, robot_type, - types["switch"], - types.get("fan")) + if CFG.env == "pybullet_domino_declare": + options.add( + create_declare_option("DeclareFinished", cfg, robot_type)) + else: + options |= cls._create_sf_switch_options( + cfg, robot_type, types["switch"], types.get("fan")) return options diff --git a/predicators/ground_truth_models/domino/predicates.py b/predicators/ground_truth_models/domino/predicates.py index f90b60665..276f916fa 100644 --- a/predicators/ground_truth_models/domino/predicates.py +++ b/predicators/ground_truth_models/domino/predicates.py @@ -19,7 +19,8 @@ class PyBulletDominoGroundTruthPredicateFactory(GroundTruthPredicateFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry", "pybullet_domino_fan" + "pybullet_domino_real_geometry", "pybullet_domino_fan", + "pybullet_domino_declare" } @classmethod diff --git a/predicators/ground_truth_models/domino/processes.py b/predicators/ground_truth_models/domino/processes.py index 4c225b503..5a3e6419b 100644 --- a/predicators/ground_truth_models/domino/processes.py +++ b/predicators/ground_truth_models/domino/processes.py @@ -97,6 +97,21 @@ def _push_sampler(state: State, goal: Set[GroundAtom], dtype=np.float32) +def _declare_sampler(state: State, goal: Set[GroundAtom], + rng: np.random.Generator, + objs: Sequence[Object]) -> Array: + """No parameters: a declaration has nothing to aim. + + Its option's params_space is empty, so an empty array is what the + option expects. Written out rather than reaching for null_sampler + to keep the contrast with _switch_push_sampler on the page: the + press needs an approach distance and a contact offset because it + has to arrive somewhere, and this does not. + """ + del state, goal, rng, objs + return np.array([], dtype=np.float32) + + def _switch_push_sampler(state: State, goal: Set[GroundAtom], rng: np.random.Generator, objs: Sequence[Object]) -> Array: @@ -185,7 +200,8 @@ class PyBulletDominoGroundTruthProcessFactory(GroundTruthProcessFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry", "pybullet_domino_fan" + "pybullet_domino_real_geometry", "pybullet_domino_fan", + "pybullet_domino_declare" } @classmethod @@ -457,17 +473,37 @@ def _get_fan_processes( processes: Set[CausalProcess] = set() - # Pressing the switch. Endogenous: the robot does it. + # Starting the fan. Endogenous either way: the robot does it. + # What differs between the two fan envs is only HOW, and so + # what the process is grounded on. robot = Variable("?robot", robot_type) fan = Variable("?fan", fan_type) - processes.add( - EndogenousProcess( - "TurnFanOn", [robot, fan], {LiftedAtom(FanOff, [fan])}, set(), - set(), {LiftedAtom(FanOn, [fan])}, {LiftedAtom(FanOff, [fan])}, - DiscreteGaussianDelay(mu=torch.tensor(1.0), - sigma=torch.tensor(0.1)), - torch.tensor(1.0), options["TurnFanOn"], [robot, fan], - _switch_push_sampler)) + if CFG.env == "pybullet_domino_declare": + # The robot announces it has finished building and the fan + # starts. The option takes only the robot -- there is + # nothing to reach for -- so the fan appears in the + # process's variables and its effects but NOT in the + # option's arguments. That split is the whole content of + # this env: an effect with no contact to explain it. + processes.add( + EndogenousProcess( + "DeclareFinished", [robot, fan], + {LiftedAtom(FanOff, [fan])}, set(), set(), + {LiftedAtom(FanOn, [fan])}, {LiftedAtom(FanOff, [fan])}, + DiscreteGaussianDelay(mu=torch.tensor(1.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["DeclareFinished"], [robot], + _declare_sampler)) + else: + processes.add( + EndogenousProcess( + "TurnFanOn", [robot, fan], {LiftedAtom(FanOff, [fan])}, + set(), set(), {LiftedAtom(FanOn, [fan])}, + {LiftedAtom(FanOff, [fan])}, + DiscreteGaussianDelay(mu=torch.tensor(1.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["TurnFanOn"], [robot, fan], + _switch_push_sampler)) # The wind. Exogenous: nobody chooses it, it follows from the # fan being on. @@ -752,7 +788,8 @@ class PyBulletDominoGroundTruthSamplerFactory(GroundTruthSamplerFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry", "pybullet_domino_fan" + "pybullet_domino_real_geometry", "pybullet_domino_fan", + "pybullet_domino_declare" } @classmethod diff --git a/predicators/ground_truth_models/domino/types.py b/predicators/ground_truth_models/domino/types.py index 6e08b2bc9..a43c54c08 100644 --- a/predicators/ground_truth_models/domino/types.py +++ b/predicators/ground_truth_models/domino/types.py @@ -19,7 +19,8 @@ class PyBulletDominoGroundTruthTypeFactory(GroundTruthTypeFactory): def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", - "pybullet_domino_real_geometry", "pybullet_domino_fan" + "pybullet_domino_real_geometry", "pybullet_domino_fan", + "pybullet_domino_declare" } @classmethod diff --git a/predicators/ground_truth_models/domino_fan/gt_simulator.py b/predicators/ground_truth_models/domino_fan/gt_simulator.py index 97045f26d..83f8e70eb 100644 --- a/predicators/ground_truth_models/domino_fan/gt_simulator.py +++ b/predicators/ground_truth_models/domino_fan/gt_simulator.py @@ -190,7 +190,11 @@ def _wind_topples_start_block(state: State, updates: ResidualUpdate, class PyBulletDominoFanGroundTruthSimulatorFactory(GroundTruthSimulatorFactory ): - """GT residual-dynamics simulator for pybullet_domino_fan. + """GT residual-dynamics simulator for the ball-free domino-fan envs. + + Both of them: the wind is the same physics whether a button press + or a declaration turned the fan on. What starts the fan is a + process, not dynamics. The simulator components (``RESIDUAL_RULES``, ``PARAM_SPECS``, ``RESIDUAL_FEATURES``) live as module globals above; this class only @@ -200,4 +204,4 @@ class PyBulletDominoFanGroundTruthSimulatorFactory(GroundTruthSimulatorFactory @classmethod def get_env_names(cls) -> set: - return {"pybullet_domino_fan"} + return {"pybullet_domino_fan", "pybullet_domino_declare"} diff --git a/predicators/ground_truth_models/skill_factories/declare.py b/predicators/ground_truth_models/skill_factories/declare.py new file mode 100644 index 000000000..b528f0bcf --- /dev/null +++ b/predicators/ground_truth_models/skill_factories/declare.py @@ -0,0 +1,114 @@ +"""Declare-finished: a skill whose whole effect is that it was run. + +The robot says "I am done building" and something in the world reacts. +It moves nothing, touches nothing, and its only trace is a flag on the +actions it emits, which the env reads in its own step. + +Why an environment would want this: pressing a physical button is easy +in simulation and awkward on real hardware -- the button has to be +reachable from every staging pose, the arm has to approach it without +sweeping through the scene it just built, and a missed press is +indistinguishable from a press that did not take. A declaration has +none of that, and for a LEARNING agent it is the more interesting +signal anyway: there is no contact to credit the effect to, so an agent +that works out "the wind starts after I declare" has found a genuinely +causal relation rather than a contact one. + +Modelled on ``wait.py``'s pose-holding policy, since the arm must stay +exactly where it is: a declaration that nudged the arm could knock the +staged chain, and then the topple would have a mechanical explanation +after all. +""" + +from typing import Dict, Optional, Sequence, Tuple, cast + +import numpy as np +from gym.spaces import Box + +from predicators import utils +from predicators.ground_truth_models.skill_factories.base import SkillConfig +from predicators.structs import DECLARE_FINISHED_KEY, Action, Array, \ + Object, ParameterizedOption, State, Type + +# Re-exported so a reader of this file finds the marker here too. An +# env that does not care ignores it, so adding this skill to a domain +# cannot change what any existing env does. +__all__ = ["DECLARE_FINISHED_KEY", "create_declare_option"] + + +def create_declare_option( + name: str, + config: SkillConfig, + robot_type: Type, + num_steps: int = 2, + params_description: Optional[Tuple[str, ...]] = None, +) -> ParameterizedOption: + """Create a no-motion option that announces itself to the env. + + Args: + name: Option name (e.g. "DeclareFinished"). + config: Shared skill configuration. See ``SkillConfig``. + robot_type: The robot ``Type`` object. + num_steps: How many actions to emit. More than one because a + single action can be swallowed by a controller that has not + settled, and the flag has to survive into a step the env + actually runs. + + Returns: + A ``ParameterizedOption`` that is always initiable and holds the + robot's pose for ``num_steps`` actions. + """ + robot = config.robot + mid_point = (config.open_fingers_joint + config.closed_fingers_joint) / 2 + + def _initiable(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> bool: + del state, objects, params + # A grounded option can be re-run (validation rollouts reuse the + # grounded plan), so the step count must not carry over. + memory["declare_steps"] = 0 + return True + + def _terminal(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> bool: + del state, objects, params + return memory.get("declare_steps", 0) >= num_steps + + def _policy(state: State, memory: Dict, objects: Sequence[Object], + params: Array) -> Action: + del params + memory["declare_steps"] = memory.get("declare_steps", 0) + 1 + robot_obj = objects[0] + # Hold the fingers where they are, exactly as Wait does: this + # skill must not disturb the scene it is declaring finished. + current_joint = config.fingers_state_to_joint( + robot, state.get(robot_obj, "fingers")) + if current_joint > mid_point: + finger_delta = config.finger_action_nudge_magnitude + else: + finger_delta = -config.finger_action_nudge_magnitude + pb_state = cast(utils.PyBulletState, state) + joint_positions = pb_state.joint_positions.copy() + f_action = joint_positions[robot.left_finger_joint_idx] + finger_delta + joint_positions[robot.left_finger_joint_idx] = f_action + joint_positions[robot.right_finger_joint_idx] = f_action + action_arr = np.array(joint_positions, dtype=np.float32) + n_action = robot.action_space.shape[0] + if action_arr.shape[0] < n_action: + action_arr = np.concatenate([ + action_arr, + np.zeros(n_action - action_arr.shape[0], dtype=np.float32) + ]) + return Action(np.clip(action_arr, robot.action_space.low, + robot.action_space.high), + extra_info={DECLARE_FINISHED_KEY: True}) + + return ParameterizedOption( + name, + types=[robot_type], + params_space=Box(0, 1, (0, )), + policy=_policy, + initiable=_initiable, + terminal=_terminal, + params_description=params_description or (), + ) diff --git a/predicators/structs.py b/predicators/structs.py index 10045d895..4ffe58c21 100644 --- a/predicators/structs.py +++ b/predicators/structs.py @@ -1988,6 +1988,15 @@ def copy_with(self, **kwargs: Any) -> _GroundNSRT: return _GroundNSRT(**default_kwargs) # type: ignore +# Key an Action carries in ``extra_info`` when the skill that produced +# it is a declaration rather than a motion: the robot announcing it has +# finished, for an env where that announcement is itself an event (see +# skill_factories/declare.py and PyBulletDominoDeclareEnv). It lives +# here, beside Action, because it is part of that contract and because +# both the skill and the env must name it without importing each other. +DECLARE_FINISHED_KEY = "declare_finished" + + @dataclass(eq=False) class Action: """An action in an environment. diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index 91e6de2a0..5169613c7 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -385,6 +385,49 @@ ENVS: # and the turn geometry was tuned for a robot push anyway. domino_test_turn_ratio: 0.0 domino_train_turn_ratio: 0.0 + # The same task with the BUTTON removed: instead of pressing a + # switch, the robot runs DeclareFinished and the fan starts. Same + # scene, same wind, same certificate - only the trigger changes. + # + # Two reasons it is its own env. A physical button is awkward on a + # real arm: it has to be reachable from every staging pose, the + # approach must not sweep through the chain just built, and a missed + # press is indistinguishable from one that did not take. And for a + # LEARNING arm the declaration is the cleaner question - nothing is + # touched, so there is no contact to credit the wind to, and an + # agent that finds the relation has found a causal one. + domino_declare: + NAME: "pybullet_domino_declare" + SKIP: True # un-skipped by run_rung.sh --declare + FLAGS: + max_initial_demos: 0 + excluded_objects_in_state_str: "loc,rot,angle,direction" + horizon: 500 + domino_initialize_at_finished_state: False + domino_use_domino_blocks_as_target: True + domino_use_continuous_place: True + process_planning_heuristic_weight: 2.0 + domino_has_glued_dominos: False + keep_failed_demos: True + predicate_invent_invent_derived_predicates: True + pybullet_birrt_extend_num_interp: 20 + pybullet_birrt_path_subsample_ratio: 2 + fan_known_controls_relation: True + fan_fans_blow_opposite_direction: False + # Lay each chain along one fan's wind axis. Without this the + # generator points chains anywhere, and a chain across the wind + # cannot cascade however hard the fan blows - the task is + # unsolvable before a planner sees it. + domino_fan_aligned_tasks: True + # Sized so the block moves before Wait declares the scene settled + # (onset 7 steps at 1.5 N against Wait's ~11), not just to clear + # the 0.123 N tipping threshold. See settings.py. + domino_fan_wind_force: 1.5 + # Straight chains only. Alignment guarantees the FIRST leg runs + # downwind; a turn puts the rest of the chain across the wind, + # and the turn geometry was tuned for a robot push anyway. + domino_test_turn_ratio: 0.0 + domino_train_turn_ratio: 0.0 # coffee: # NAME: "pybullet_coffee" # FLAGS: diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index 226c018bc..fbf71db15 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -7,6 +7,11 @@ # scripts/domino_fan/run_rung.sh 4 # learns wind code + predicates # scripts/domino_fan/run_rung.sh all # 1 then 2 then 3 then 4 # +# Add --declare for the button-free variant, where the robot runs +# DeclareFinished instead of pressing a switch: +# +# scripts/domino_fan/run_rung.sh --declare 1 +# # Each rung gives the agent less and asks it to recover more: # 1 GT simulator + GT predicates, process planner plans. # 2 GT simulator + GT predicates, the AGENT plans. @@ -21,7 +26,15 @@ cd "$(dirname "$0")/../.." PY=".venv/bin/python" [ -x "$PY" ] || PY="python3" -usage() { sed -n '2,18p' "$0" | sed 's/^# \{0,1\}//'; exit 1; } +usage() { sed -n '2,24p' "$0" | sed 's/^# \{0,1\}//'; exit 1; } + +# Which env the ladder runs on. Same arms, same rungs; only the thing +# that starts the fan differs. +ENV_KEY="domino_fan" +if [ "${1:-}" = "--declare" ]; then + ENV_KEY="domino_declare" + shift +fi [ $# -ge 1 ] || usage run_one() { @@ -42,11 +55,11 @@ run_one() { # experiment_id is built by cluster_utils as "-", # so it is domino_fan-; the dashboard maps that back to a rung. - local cfg="scripts/configs/predicatorv3/_rung${rung}.yaml" + local cfg="scripts/configs/predicatorv3/_${ENV_KEY}_rung${rung}.yaml" # Generated, not hand-edited: every arm parked except this one. - $PY - "$rung" "$arm" > "$cfg" <<'PYEOF' + $PY - "$rung" "$arm" "$ENV_KEY" > "$cfg" <<'PYEOF' import sys -rung, arm = sys.argv[1], sys.argv[2] +rung, arm, env_key = sys.argv[1], sys.argv[2], sys.argv[3] arms = ["oracle", "agent_model_based_planning", "agent_param_learning", "agent_po_predicate_invention_al"] lines = [ @@ -58,7 +71,7 @@ lines = [ " - envs/all.yaml", " - approaches/all.yaml", "ENVS:", - " domino_fan:", + f" {env_key}:", " SKIP: False", "APPROACHES:", ] @@ -90,9 +103,9 @@ PYEOF echo "==========================================================" echo " RUNG $rung -> $arm" - echo " logs/*/domino_fan-$arm/ (rung $rung: $exp)" + echo " logs/*/${ENV_KEY}-$arm/ (rung $rung: $exp)" echo "==========================================================" - PYTHONHASHSEED=0 $PY scripts/local/launch_simp.py -c "predicatorv3/_rung${rung}.yaml" + PYTHONHASHSEED=0 $PY scripts/local/launch_simp.py -c "predicatorv3/_${ENV_KEY}_rung${rung}.yaml" } if [ "$1" = "all" ]; then From b46270ac12dc17438cd8a43b67768f049988f0dd Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 11:53:31 -0400 Subject: [PATCH 34/48] domino_fan: the goal text was handing over the mechanism 'when the fan is switched on, THE WIND TOPPLES THE GREEN DOMINO and the purple domino is toppled' told every agent the causal rule that rung 4 exists to discover. The predicate-invention run then reported 'the wind mechanism is absent from the belief model' as a finding, having been told in its own goal statement what to look for. Both fan envs now state the goal and the rules and stop there: what must end up toppled, what may be rearranged, what may not move before the trigger, and which step is sanctioned to start things. How the trigger reaches the dominoes is the thing being learned, so it is no longer written down. The verification clauses drop the word too - they name the trigger, not the mechanism. Rungs 1 and 2 are unaffected in substance: they are handed the ground-truth model anyway, so the sentence told them nothing they did not already have. The dashboard now badges each row with its env's trigger, BUTTON or DECLARE, so the two ladders read as two series rather than one. Declare oracle: 1/1 at reward 0.900, same as the button oracle - the robot picks, places one blue, declares finished, and the cascade runs. --- .../task_generators/domino_task_generator.py | 10 ++--- .../task_generators/goal_text.py | 6 ++- .../predicatorv3/_domino_declare_rung1.yaml | 21 ++++++++++ .../predicatorv3/_domino_declare_rung2.yaml | 22 ++++++++++ scripts/log_viewer.py | 40 +++++++++++++++---- 5 files changed, 84 insertions(+), 15 deletions(-) create mode 100644 scripts/configs/predicatorv3/_domino_declare_rung1.yaml create mode 100644 scripts/configs/predicatorv3/_domino_declare_rung2.yaml diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index 74cb946f3..90e7b9148 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -242,9 +242,9 @@ def _generate_single_task( if fan_only and trigger == "DeclareFinished": goal_nl = ( f"Arrange the blue dominoes as needed (possibly none) such " - f"that once you declare you have finished building, the " - f"wind topples the green domino and {target_word} " - f"{target_verb} toppled. Only the blue dominoes may be " + f"that once you declare you have finished building, " + f"{target_word} {target_verb} toppled. Only the blue " + f"dominoes may be " f"rearranged: the green and purple dominoes must stay " f"untouched at their staged poses, upright and never held, " f"until you declare finished, and nothing may topple " @@ -254,8 +254,8 @@ def _generate_single_task( elif fan_only: goal_nl = ( f"Arrange the blue dominoes as needed (possibly none) such " - f"that when the fan is switched on, the wind topples the " - f"green domino and {target_word} {target_verb} toppled. " + f"that when the fan is switched on, {target_word} " + f"{target_verb} toppled. " f"Only the blue dominoes may be rearranged: the green and " f"purple dominoes must stay untouched at their staged " f"poses, upright and never held, until the fan is switched " diff --git a/predicators/envs/pybullet_domino/task_generators/goal_text.py b/predicators/envs/pybullet_domino/task_generators/goal_text.py index 5de7ffab8..97a979cd0 100644 --- a/predicators/envs/pybullet_domino/task_generators/goal_text.py +++ b/predicators/envs/pybullet_domino/task_generators/goal_text.py @@ -27,7 +27,8 @@ # reached the goal by knocking the chain over while placing a block # would certify. WIND_VERIFICATION_NL = ( - " A solve only counts if the wind starts the cascade: the episode " + " A solve only counts if switching the fan on is what starts the " + "cascade: the episode " "must carry a TurnFanOn step, nothing may topple before it, and an " "episode that reaches the goal with no TurnFanOn on the record is " "rejected.") @@ -46,7 +47,8 @@ # checked is that the declaration is on the record and that nothing # fell before it. DECLARE_VERIFICATION_NL = ( - " A solve only counts if the wind starts the cascade: the episode " + " A solve only counts if the declaration is what starts the " + "cascade: the episode " "must carry a DeclareFinished step, nothing may topple before it, " "and an episode that reaches the goal with no DeclareFinished on " "the record is rejected.") diff --git a/scripts/configs/predicatorv3/_domino_declare_rung1.yaml b/scripts/configs/predicatorv3/_domino_declare_rung1.yaml new file mode 100644 index 000000000..58ab0a207 --- /dev/null +++ b/scripts/configs/predicatorv3/_domino_declare_rung1.yaml @@ -0,0 +1,21 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 1. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_declare: + SKIP: False +APPROACHES: + oracle: + SKIP: False + FLAGS: + option_model_use_gui: False + agent_model_based_planning: + SKIP: True + agent_param_learning: + SKIP: True + agent_po_predicate_invention_al: + SKIP: True diff --git a/scripts/configs/predicatorv3/_domino_declare_rung2.yaml b/scripts/configs/predicatorv3/_domino_declare_rung2.yaml new file mode 100644 index 000000000..a9e590505 --- /dev/null +++ b/scripts/configs/predicatorv3/_domino_declare_rung2.yaml @@ -0,0 +1,22 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 2. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_declare: + SKIP: False +APPROACHES: + oracle: + SKIP: True + agent_model_based_planning: + SKIP: False + FLAGS: + option_model_use_gui: False + num_online_learning_cycles: 0 + agent_param_learning: + SKIP: True + agent_po_predicate_invention_al: + SKIP: True diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index a7147345f..d93e9ef3a 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1808,6 +1808,14 @@ def ansi_to_html(text: str) -> str: /* The compare column: an unlabeled checkbox is a question, not a control. Small and grey, but named. */ th.cmphdr { font-size: 9px; letter-spacing: .08em; cursor: help; } +/* Which env a run is on, beside its rung: same ladder, different + trigger. Coloured apart so the two are never read as one series. */ +.trig { display: inline-block; margin-right: 6px; padding: 1px 6px; + border-radius: 4px; font-size: 9px; font-weight: 700; + letter-spacing: .06em; cursor: help; + border: 1px solid var(--border2); color: var(--muted); } +.trig-declare { border-color: var(--ok); color: var(--ok); + background: var(--ok-dim); } /* The "what is happening right now" panel above the run table. */ .livepanel { border: 1px solid var(--border); border-radius: 8px; background: var(--panel); padding: 12px 14px; margin: 0 0 14px; } @@ -2423,14 +2431,25 @@ def status_chip(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, # The domino-fan ladder, keyed by the arm that experiment_id carries. # Each rung hands the agent less; the label says what it must supply, so # a row reads as a rung of the experiment rather than an approach name. +# The ladder is the same on both fan envs, so the rung comes from the +# ARM and the env is shown beside it: what differs between the two is +# not what the agent is given but what starts the cascade. _RUNG_BY_ARM = { - "domino_fan-oracle": (1, "GT sim + GT predicates, process planner plans"), - "domino_fan-agent_model_based_planning": + "oracle": (1, "GT sim + GT predicates, process planner plans"), + "agent_model_based_planning": (2, "GT sim + GT predicates, the AGENT plans"), - "domino_fan-agent_param_learning": + "agent_param_learning": (3, "GT sim structure; must fit the wind's parameters"), - "domino_fan-agent_po_predicate_invention_al": - (4, "base sim only; must find the wind, model it, invent predicates"), + "agent_po_predicate_invention_al": + (4, "base sim only; must find the mechanism and invent predicates"), +} + +# How the cascade is started, which is the whole difference between the +# two envs and so the thing a reader most needs on the row. +_TRIGGER_BY_ENV = { + "domino_fan": ("BUTTON", "the robot presses a switch to start the fan"), + "domino_declare": + ("DECLARE", "no switch: the robot declares finished and the fan starts"), } @@ -2565,11 +2584,16 @@ def live_panel(runs: List[Dict[str, Any]], summaries: Dict[str, Dict[str, def rung_badge(exp: str) -> str: """"RUNG n" chip for a domino-fan experiment, else empty.""" key = exp.split("/")[-1] - entry = _RUNG_BY_ARM.get(key) - if entry is None: + env_key, _, arm = key.partition("-") + entry = _RUNG_BY_ARM.get(arm) + trigger = _TRIGGER_BY_ENV.get(env_key) + if entry is None or trigger is None: return "" num, what = entry - return f"RUNG {num}" + word, how = trigger + cls = "trig trig-declare" if env_key == "domino_declare" else "trig" + return (f"RUNG {num}" + f"{word}") def run_row(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, From 72b84655d33de2c1ce2e8f68b3f801cc87a33a3c Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 12:33:54 -0400 Subject: [PATCH 35/48] domino_declare: it is wind-started too, its name just does not say so wait_quiescence_steps was chosen by CFG.env.endswith('_fan'), which gave pybullet_domino_declare the push threshold of 10. A wind-started cascade has a lull built into it - the fan cuts out the moment the start block is down, and the chain then coasts on contact alone - so 10 quiet steps reads as settled and ends the Wait mid-cascade. The envs are named explicitly now. Found by the agent in run_20260831_122006, which noticed the config value, worked out what it implied, and padded its plan with extra Waits rather than failing. Its rungs 1 and 2 had passed anyway, so nothing had flagged it. --- .../ground_truth_models/domino/options.py | 16 +++++++++++++- .../predicatorv3/_domino_declare_rung4.yaml | 22 +++++++++++++++++++ 2 files changed, 37 insertions(+), 1 deletion(-) create mode 100644 scripts/configs/predicatorv3/_domino_declare_rung4.yaml diff --git a/predicators/ground_truth_models/domino/options.py b/predicators/ground_truth_models/domino/options.py index 5ea730b41..5a111eb0a 100644 --- a/predicators/ground_truth_models/domino/options.py +++ b/predicators/ground_truth_models/domino/options.py @@ -40,6 +40,19 @@ def _skill_robot_env_cls(env_name: str) -> TypingType[PyBulletEnv]: return PyBulletDominoEnv +# Envs where the cascade is started by WIND rather than by a push, and +# so where a Wait has to outlast the lull described below. Named +# explicitly because the obvious test - a "_fan" suffix - silently gave +# pybullet_domino_declare the push threshold of 10: it is wind-started +# too, it just does not say so in its name. The agent in +# run_20260831_122006 noticed before I did, and padded its plan with +# extra Waits to compensate. +_WIND_STARTED_ENVS = frozenset({ + "pybullet_domino_fan", + "pybullet_domino_declare", +}) + + class PyBulletDominoGroundTruthOptionFactory(_DominoLegacyOptionsMixin, GroundTruthOptionFactory): """Ground-truth options for the domino environment.""" @@ -256,7 +269,8 @@ def _build_skill_config( # and the chain then coasts on contact alone. Ten steps of # that reads as settled and ends the Wait mid-cascade - # measured at 35 steps against the ~70 the chain needs. - wait_quiescence_steps=(40 if CFG.env.endswith("_fan") else 10), + wait_quiescence_steps=(40 if CFG.env in _WIND_STARTED_ENVS + else 10), ) @classmethod diff --git a/scripts/configs/predicatorv3/_domino_declare_rung4.yaml b/scripts/configs/predicatorv3/_domino_declare_rung4.yaml new file mode 100644 index 000000000..41f9b3b22 --- /dev/null +++ b/scripts/configs/predicatorv3/_domino_declare_rung4.yaml @@ -0,0 +1,22 @@ +# GENERATED by scripts/domino_fan/run_rung.sh for rung 4. +# Edit run_rung.sh, not this file - it is overwritten on every run. +--- +includes: + - common.yaml + - envs/all.yaml + - approaches/all.yaml +ENVS: + domino_declare: + SKIP: False +APPROACHES: + oracle: + SKIP: True + agent_model_based_planning: + SKIP: True + agent_param_learning: + SKIP: True + agent_po_predicate_invention_al: + SKIP: False + FLAGS: + option_model_use_gui: False + skip_initial_test: True From ed156d36a85322528ccd14db150ac63ae461bd35 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 14:09:59 -0400 Subject: [PATCH 36/48] domino_declare: keep the simulator the agent discovered run_20260831_122006 worked out, from two recorded trajectories and nothing else, that declaring finished switches on a fan whose wind topples the start block. Its own decision record states the inference: the same action sequence flips fan_0.is_on 0->1 one step after the first DeclareFinished, 'and then the green start block tips over ... with NO contact and NO robot nearby'. The harder half is that the trigger leaves no trace. Residual rules never see actions, and a declaration is - to the base sim - indistinguishable from a two-step Wait, so 'has the agent declared?' is hidden state. It inferred the event from a stillness signature it fitted itself: two clean clusters of arm movement separated by a factor of ~34, cut at 0.002, giving a latch that fires exactly one step before the recorded flip. The run is stopped rather than finished: its recorded cascades were truncated by the wait_quiescence_steps bug it had itself pointed out, and the fit dropped one of two trajectories as unexplainable. The result worth keeping is the reasoning, not a reward number. --- .../domino_declare/discovered_simulator.py | 238 ++++++++++++++++++ 1 file changed, 238 insertions(+) create mode 100644 docs/envs/domino_declare/discovered_simulator.py diff --git a/docs/envs/domino_declare/discovered_simulator.py b/docs/envs/domino_declare/discovered_simulator.py new file mode 100644 index 000000000..3dced0296 --- /dev/null +++ b/docs/envs/domino_declare/discovered_simulator.py @@ -0,0 +1,238 @@ +# ===================================================================== +# DECISION RECORD - pybullet_domino_declare (cycle 3, learn) +# ===================================================================== +# WHAT THE BASE SIM ALREADY DOES (verified, do not re-implement): +# * All rigid-body motion: arm IK, grasping, transport, release, and +# the *entire* domino-on-domino cascade. Cycle-1/2 probes showed +# that seeding `mods={'domino_0': {'roll': 0.3}}` makes the base sim +# topple green -> blue -> purple all by itself. Contact physics is +# present and good. +# +# WHAT IS MISSING (measured this cycle, probe with an empty rule set): +# Running `DeclareFinished` + `Wait` from task-0 init in the base sim +# leaves fan_0.is_on = 0.0 and every domino untouched. In the RECORDED +# env trajectories the exact same action sequence flips +# fan_0.is_on 0->1 and switch_0.is_on 0->1 one step after the first +# DeclareFinished action (traj0 t=74, traj1 t=80) and then the green +# start block tips over: roll 0 -> .047 -> .128 -> .193 -> .294 (and +# x drifts +x, toward the target) with NO contact and NO robot nearby. +# => An exogenous influence the engine knows nothing about: the +# declaration switches a FAN on and its wind topples the start +# block. Diagnostic ladder case 2 -> model with force `cmds`, +# gated on an observable condition; plus the device-state flip on +# the feature-update channel. +# +# THE TRIGGER IS NOT DIRECTLY OBSERVABLE (partial observability): +# Rules never see actions, and `DeclareFinished` is - to the base sim +# - indistinguishable from a 2-step Wait (probe diff: only sub-mm +# arm/finger drift). So "has the agent declared?" is a HIDDEN state. +# It is inferred from an observable signature and carried in `latent` +# (Pattern A: counter + threshold). +# Signature = "robot holds nothing AND the arm has stopped moving". +# Threshold-fitting protocol, traj0, max |delta| over robot +# (x,y,z,roll,tilt,wrist), buckets over the is_held==0 steps: +# arm-moving bucket (t=1..20, 60..72): min 0.0086, typ 0.02-1.0 +# arm-still bucket (t=73..78, declare/wait): max 0.000256 +# -> two clean clusters separated by a factor of ~34. Cut at 0.002. +# Held steps (t=21..59) are excluded by the is_held gate, which is +# what makes the slow 0.0007/step transport phase harmless. +# With `declare_delay`=1 the latch fires on state[73] and the fan +# reads on at state[74] - exactly the recorded flip step. +# +# WHAT THE RULES OWN (RESIDUAL_FEATURES): +# fan.is_on, switch.is_on - written directly (base sim never sets +# them; they are the device readout of +# the latent `declared` flag). +# domino.x/z/roll - moved by the wind FORCE through the +# engine, so they are SCORED but not +# overwritten. Listing them is what gives +# the wind magnitude an SSE signal. +# (domino.y / yaw deliberately omitted: the wind is axial, those +# carry only base-sim replay noise from the Pick/Place phase.) +# +# WIND MODEL - simplest hypothesis that fits: +# Constant world-frame force along the fan axis (cos rot, sin rot), +# applied every step the fan is on, to ONE domino: the "start block", +# latched at declare time as the nearest not-yet-toppled domino +# inside a narrow beam in front of the fan (that is domino_0, the +# green one, at lateral offset 0.000; the blue sits 0.017-0.025 off +# axis and never moves under wind in either recorded trajectory). +# The force stops once the start block is down - matching the env +# note in reference/options.py that "the fan cuts out the moment the +# start block is down (a fallen domino is out of the airstream)". +# Everything after that - green hitting blue hitting purple - is left +# entirely to the base sim's contact physics. +# +# PHYSICAL_PARAMS: not declared (see the sweep in the session log). +# The base sim reproduces the recorded rigid-body motion; the defect +# was a missing mechanism, not mis-set physics. +# ===================================================================== + +# --- object/feature helpers ------------------------------------------ + +_ARM_FEATS = ("x", "y", "z", "roll", "tilt", "wrist") + + +def _by_type(state, tname): + return [o for o in state.data if o.type.name == tname] + + +def _prev_obs(observation, history): + """Most recent *earlier* observation, or None at the first step.""" + if not history: + return None + for entry in reversed(history): + st = entry[0] if isinstance(entry, (tuple, list)) else entry + if st is not observation: + return st + return None + + +def _is_toppled(state, dom, params): + return abs(state.get(dom, "roll")) > params["topple_roll"] + + +def _fan_axis(state, fan): + rot = state.get(fan, "rot") + return float(np.cos(rot)), float(np.sin(rot)) + + +def _fan_anchor(state, fan, params): + """Outlet point of the fan: recorded origin + a LOCAL-frame offset + rotated by the fan's `rot`. Shared with predicates.InAirstream.""" + ux, uy = _fan_axis(state, fan) + ox = params["fan_local_dx"] * ux - params["fan_local_dy"] * uy + oy = params["fan_local_dx"] * uy + params["fan_local_dy"] * ux + return state.get(fan, "x") + ox, state.get(fan, "y") + oy + + +def _beam_pick(state, fan, dominoes, params): + """Nearest upright, un-held domino inside the fan's beam.""" + ux, uy = _fan_axis(state, fan) + fx, fy = _fan_anchor(state, fan, params) + best, best_along = None, None + for d in dominoes: + if state.get(d, "is_held") > 0.5: + continue + if _is_toppled(state, d, params): + continue + dx = state.get(d, "x") - fx + dy = state.get(d, "y") - fy + along = dx * ux + dy * uy + lateral = -dx * uy + dy * ux + if along <= 0.0: + continue + if abs(lateral) > params["beam_halfwidth"]: + continue + if best_along is None or along < best_along: + best, best_along = d, along + return best + + +# --- rule 1: infer the hidden "declared" flag, drive the devices ----- + + +def declare_rule(observation, latent, history, updates, params): + """Latent Pattern A: count steps of 'nothing held + arm stopped'; + latch `declared` once the count passes the learned delay, and read + the latch out onto every fan / switch `is_on`.""" + robots = _by_type(observation, "robot") + dominoes = _by_type(observation, "domino") + + held = any(observation.get(d, "is_held") > 0.5 for d in dominoes) + prev = _prev_obs(observation, history) + still = False + if prev is not None and not held: + try: + drift = max( + abs(observation.get(r, f) - prev.get(r, f)) + for r in robots for f in _ARM_FEATS) + still = drift < params["still_eps"] + except Exception: # pylint: disable=broad-except + still = False + + latent["still"] = (latent.get("still", 0.0) + 1.0) if still else 0.0 + if latent.get("declared", 0.0) < 0.5: + if latent["still"] >= params["declare_delay"]: + latent["declared"] = 1.0 + + # Also honour a directly observed device flip (recorded env data + # sets is_on itself); never un-latch. + for fan in _by_type(observation, "fan"): + if observation.get(fan, "is_on") > 0.5: + latent["declared"] = 1.0 + + on = 1.0 if latent.get("declared", 0.0) > 0.5 else 0.0 + for fan in _by_type(observation, "fan"): + updates.setdefault(fan, {})["is_on"] = on + for sw in _by_type(observation, "switch"): + updates.setdefault(sw, {})["is_on"] = on + return updates + + +# --- rule 2: the wind itself (physics-command channel) --------------- + + +def wind_rule(observation, latent, history, updates, params, cmds): + """Constant axial force on the latched start block while the fan is + on and that block is still standing.""" + del history + if latent.get("declared", 0.0) < 0.5: + return updates + dominoes = _by_type(observation, "domino") + if not dominoes: + return updates + by_name = {d.name: d for d in dominoes} + + for fan in _by_type(observation, "fan"): + key = "start_" + fan.name + target = by_name.get(latent.get(key)) + if target is None: + target = _beam_pick(observation, fan, dominoes, params) + if target is None: + continue + latent[key] = target.name + # The fan cuts out once the start block is down / out of the + # airstream; the chain then coasts on contact alone. + if _is_toppled(observation, target, params): + continue + if observation.get(target, "is_held") > 0.5: + continue + ux, uy = _fan_axis(observation, fan) + f = params["wind_force"] + cmds.apply_force(target, (ux * f, uy * f, 0.0)) + return updates + + +RESIDUAL_RULES = [declare_rule, wind_rule] + +PARAM_SPECS = [ + # "arm has stopped" cut: still bucket <= 2.6e-4, moving bucket + # >= 8.6e-3 (traj0, is_held==0 steps). + ParamSpec("still_eps", 0.002, lo=0.0005, hi=0.006), + # Steps of stillness before the declaration is inferred. + ParamSpec("declare_delay", 1.0, lo=1.0, hi=6.0), + # Newtons of wind on the start block. + ParamSpec("wind_force", 0.20, lo=0.0, hi=1.5), + # Half-width of the airstream, metres, about the fan axis. + ParamSpec("beam_halfwidth", 0.04, lo=0.005, hi=0.15), + # |roll| beyond which a domino counts as down / out of the stream. + ParamSpec("topple_roll", 0.60, lo=0.2, hi=1.4), + # Fan outlet offset in the fan's LOCAL frame (shared with the + # InAirstream predicate). Init 0: the render shows the housing + # centred on its recorded origin, so the fit is free to confirm it. + ParamSpec("fan_local_dx", 0.0, lo=-0.2, hi=0.2), + ParamSpec("fan_local_dy", 0.0, lo=-0.2, hi=0.2), + # Predicate-only (no SSE signal -> stays at init_value): the + # centre-to-centre gap a toppling domino can still bridge. + # Cycle-1/2 reach sweeps: 0.130 m links propagate, 0.140 m do not. + ParamSpec("chain_gap_max", 0.125, lo=0.05, hi=0.20), +] + +LATENT_INIT = {"declared": 0.0, "still": 0.0} + +RESIDUAL_FEATURES = { + "fan": ["is_on"], + "switch": ["is_on"], + "domino": ["x", "z", "roll"], +} From a1580d4c03c9e475e7a9a005af5e40ab69a610fe Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 20:24:43 -0400 Subject: [PATCH 37/48] domino_fan: the wind, discovered from scratch and better than mine run_20260831_152630, given the base simulator and a goal text that never mentions wind or fans, wrote the residual dynamics itself. It found the mechanism: 'Exactly one step after fan_0.is_on flips to 1 (t=98->99 in traj 0, t=112->113 in traj 1) the GREEN domino - the one nearest the fan along the fan's facing axis - begins to translate in +x and to roll ... Nothing else moves.' It got the modelling decision right for the right reason: a force through cmds, 'NOT a feature overwrite - the engine must resolve the resulting contacts (that is what produces the cascade)'. And it found something my hand-written GT simulator does not model at all. The wind is OCCLUDED: the staged blue sits on the fan's axis and shows roll exactly 0.000 until the toppling green reaches it, and 'a sub-threshold force would have produced a visible lean', so an upright domino blocks the beam. My version cheats past this by applying wind only to whichever block is painted green. The agent derived the general rule from the data. Two explore episodes reached the goal at reward 0.95 - the same one-block solve rung 2 gets while holding the ground-truth model. --- docs/envs/domino_fan/discovered_simulator.py | 127 +++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 docs/envs/domino_fan/discovered_simulator.py diff --git a/docs/envs/domino_fan/discovered_simulator.py b/docs/envs/domino_fan/discovered_simulator.py new file mode 100644 index 000000000..6fa01510c --- /dev/null +++ b/docs/envs/domino_fan/discovered_simulator.py @@ -0,0 +1,127 @@ +"""Residual dynamics for pybullet_domino_fan. + +DECISION RECORD +=============== +Evidence (2 successful interaction trajectories, task 0, reward 0.95 each): + +* Base sim carries: robot motion, grasping (Pick/Place), and ALL rigid-body + domino-on-domino cascade physics. In both trajectories the blue domino is + picked from (0.47, 1.275) and placed between the green (x=0.54) and the + purple (x=0.736); the recorded contact-driven part of the cascade + (green -> blue -> purple, rolls ramping to ~1.57 within 3-4 steps of each + contact) is ordinary rigid-body physics and needs no rule. + +* MISSING mechanism = the fan's wind. Exactly one step after fan_0.is_on + flips to 1 (t=98 -> 99 in traj 0, t=112 -> 113 in traj 1) the GREEN domino - + the one nearest the fan along the fan's facing axis - begins to translate + in +x and to roll (roll 0 -> 0.047 -> 0.126 -> 0.19 -> 0.29 -> 0.49 ...), + with an essentially identical profile in both trajectories. Nothing else + moves. The base sim replays this segment inert (confirmed by rollout probe + and by two earlier cycles that saw no topple at all with the fan on), so + this is diagnostic-ladder case 2: an exogenous influence the engine knows + nothing about. It is modelled with a constant `cmds.apply_force` gated on + `is_on`, NOT with a feature overwrite - the engine must resolve the + resulting contacts (that is what produces the cascade). + +* Wind is OCCLUDED, not range-limited-only. The placed blue domino sits + 0.30-0.32 m from the fan, directly on the fan's axis, and shows roll + EXACTLY 0.000 until the toppling green physically reaches it. A + sub-threshold force would have produced a visible lean, so the blue + receives no wind at all while the upright green stands in front of it. + The rule therefore applies wind only to a domino that has no upright + domino between it and the fan (lateral overlap < `wind_beam_halfwidth`), + plus a generous learnable `wind_range` cutoff. + +* Latent state: NONE needed. Every driver of the residual is observable + (fan.is_on, fan.rot, domino poses). `latent` is threaded but unused. + +* PHYSICAL_PARAMS: not declared (see rollout sweep evidence in the session); + the cascade geometry the base sim produces already matches the recorded + contact chain once the wind force exists. Re-examine only if a calibrated + wind still fails to propagate. +""" + +from typing import Any, Dict, List + +import numpy as np + +# ---------------------------------------------------------------- params ---- +PARAM_SPECS = [ + # Constant wind force (N) applied to the exposed domino while the fan is on. + ParamSpec("wind_force", 0.18, lo=0.179999, hi=0.180001), + # Reach of the wind measured along the fan's facing axis (m). + ParamSpec("wind_range", 0.60, lo=0.05, hi=1.5), + # Half-width of the wind beam, lateral to the facing axis (m). + ParamSpec("wind_beam_halfwidth", 0.06, lo=0.01, hi=0.30), + # |roll| above which a domino counts as toppled (no longer blocks wind, + # and, for predicates, counts as "down"). + ParamSpec("topple_roll", 0.60, lo=0.15, hi=1.40), + # Upright tolerance: |roll| below this means "still standing". + ParamSpec("upright_roll", 0.10, lo=0.01, hi=0.40), +] + +LATENT_INIT: Dict[str, Any] = {} + +RESIDUAL_FEATURES = {"domino": ["x", "z", "roll"]} + + +# ----------------------------------------------------------------- rules ---- +def _wind_axis(observation, fan): + rot = float(observation.get(fan, "rot")) + return np.array([np.cos(rot), np.sin(rot)]) + + +def wind_rule(observation, latent, history, updates, params, cmds): + """Constant wind force on the domino at the FRONT of a running fan's beam. + + Conservative by construction: only the frontmost domino in the beam is + ever pushed (that is all the data shows moving), so every downstream + topple must be earned by a real contact in the engine. The force tapers + with cos(roll) - the projected frontal area of a tipping domino - which + is what stops it once the domino is on the table. + """ + del latent, history + fans = [o for o in observation.data if o.type.name == "fan"] + dominoes = [o for o in observation.data if o.type.name == "domino"] + if not fans or not dominoes: + return updates + + for fan in fans: + if float(observation.get(fan, "is_on")) <= 0.5: + continue + axis = _wind_axis(observation, fan) + perp = np.array([-axis[1], axis[0]]) + origin = np.array([ + float(observation.get(fan, "x")), + float(observation.get(fan, "y")), + ]) + + best = None # (along, domino, roll) + for d in dominoes: + if float(observation.get(d, "is_held")) > 0.5: + continue + v = np.array([ + float(observation.get(d, "x")), + float(observation.get(d, "y")), + ]) - origin + along = float(v @ axis) + lateral = abs(float(v @ perp)) + if along <= 0.0 or along > params["wind_range"]: + continue + if lateral > params["wind_beam_halfwidth"]: + continue + roll = abs(float(observation.get(d, "roll"))) + if best is None or along < best[0]: + best = (along, d, roll) + if best is None: + continue + _, d, roll = best + taper = max(0.0, float(np.cos(roll))) + f = params["wind_force"] * taper + if f <= 0.0: + continue + cmds.apply_force(d, (float(axis[0] * f), float(axis[1] * f), 0.0)) + return updates + + +RESIDUAL_RULES: List[Any] = [wind_rule] From 7540cac88a8af927dd547019a912cf9fcaaaec9d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Mon, 31 Aug 2026 20:26:44 -0400 Subject: [PATCH 38/48] domino_fan: let rung 4 finish It spent five hours inside cycle 0 and never scored a test. The cost is the rollout fit: the agent declared five physical params (the hand-written GT simulator has two), and grid seeding is a coordinate sweep - params x seed_points x passes evaluations, each rebuilding a PyBullet env and replaying every recorded trajectory, run once per refit. That fit was buying nothing. It reported a delta of exactly 0.0000 on all five params, five times over, which is the identifiability wall probe_wind_identifiability.py measures: above the topple threshold the wind's effect saturates and SSE is flat. Meanwhile the agent's OWN initial values had already produced two real-env episodes at reward 0.95 - the same one-block solve rung 2 gets while holding the ground-truth model. So the fit budget is cut and the cycle count with it. What this rung is for - discovering the mechanism and inventing the predicates to express it - happens in cycle 0 either way. --- scripts/domino_fan/run_rung.sh | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index fbf71db15..03fbf2f20 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -97,6 +97,27 @@ for a in arms: flags.append(" num_online_learning_cycles: 0") elif a.startswith("agent_p"): flags.append(" skip_initial_test: True") + if a == "agent_po_predicate_invention_al": + # Rung 4 spent 5 hours inside cycle 0 and never scored a test. + # The cost is the rollout fit: the agent declared FIVE physical + # params (the hand-written GT simulator has two), and the grid + # seeding is a coordinate sweep - params x seed_points x passes + # rollout evaluations, each rebuilding a PyBullet env, each + # replaying every recorded trajectory, run once per refit. + # + # Worth cutting because that fit was not buying anything: it + # reported a Δ of exactly 0.0000 on all five params, five times + # over, which is the same identifiability wall + # probe_wind_identifiability.py measures - above the topple + # threshold the wind's effect saturates and SSE is flat. The + # discovery this rung is for happens in cycle 0 regardless, and + # the agent's own initial values already produced reward-0.95 + # episodes in the real env. + flags += [ + " num_online_learning_cycles: 2", + " code_sim_learning_rollout_grid_seed_points: 5", + " code_sim_learning_rollout_grid_sweep_passes: 1", + ] lines += [" FLAGS:"] + flags print("\n".join(lines)) PYEOF From 20669ae7829ad967e56e67b4458a7931e91b644d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 00:15:57 -0400 Subject: [PATCH 39/48] domino_fan: write up the ladder, and what rung 4 found Rung 4 scored 1/1 at reward 0.950 from the base simulator alone - matching rung 2, which holds the ground-truth model, and beating the oracle's 0.900 by spending one blue where the process planner spends two. Its simulator and predicates are kept here beside the GT ones, because on one point the agent's is better: it models the wind as occluded, an upright domino blocking the beam, proven by noting that a staged blue on the fan axis holds roll exactly 0.000 until the green reaches it and that a sub-threshold force would have shown a lean. The hand-written version applies wind only to whichever block is painted green, which is a role lookup rather than physics. The README now says why rung 3 is skipped rather than leaving a gap in the table that reads as unfinished work. --- docs/envs/domino_fan/README.md | 55 +++- docs/envs/domino_fan/discovered_predicates.py | 119 ++++++++ docs/envs/domino_fan/discovered_simulator.py | 257 +++++++++++------- 3 files changed, 326 insertions(+), 105 deletions(-) create mode 100644 docs/envs/domino_fan/discovered_predicates.py diff --git a/docs/envs/domino_fan/README.md b/docs/envs/domino_fan/README.md index 04fa687c8..8164aa241 100644 --- a/docs/envs/domino_fan/README.md +++ b/docs/envs/domino_fan/README.md @@ -8,11 +8,60 @@ only way to start a cascade is the fan. Run it: ```bash -PYTHONHASHSEED=0 .venv/bin/python scripts/local/launch_simp.py \ - -c predicatorv3/exp_domino_fan.yaml +scripts/domino_fan/run_rung.sh 1 # or 2, 4 +scripts/domino_fan/run_rung.sh --declare 1 # the button-free variant ``` -`exp_domino_fan.yaml` carries the whole ladder; un-skip one arm at a time. +## Results + +| rung | what it is handed | result | +|-----:|-------------------|--------| +| 1 | ground-truth simulator and predicates; the process planner plans | 1/1 · **0.900** | +| 2 | ground-truth simulator and predicates; the AGENT plans | 1/1 · **0.950** | +| 3 | structure only, parameters fitted from data | *skipped, see below* | +| 4 | **the base simulator alone** | 1/1 · **0.950** | + +Reward is `1 - 0.05 x blues consumed`, so 0.950 is a one-block solve +and 0.900 a two-block one. Rung 4 matches rung 2 and beats the oracle +while being handed none of the model. + +**Rung 3 is skipped on purpose.** Fitting the wind's magnitude is not +a learnable problem here: the wind acts for about two steps before the +start block tips, so the whole observation is "tipped or did not", and +1.5 N and 2.0 N produce identical trajectories. Measure it yourself +with `scripts/domino_debug/probe_wind_identifiability.py`. Contrast +`pybullet_fan`, which fits the same parameter happily because a ball's +entire trajectory is wind. What a wind parameter is pushing decides +whether it can be fitted. + +## What rung 4 discovered + +`discovered_simulator.py` and `discovered_predicates.py` in this +directory are the agent's own, copied from run_20260831_202652. Its +goal text never contained the words "wind" or "fan", and its predicate +vocabulary was stripped to `Holding` alone. + +It found the mechanism by comparing its model against reality: "exactly +one step after `fan_0.is_on` flips to 1 ... the GREEN domino - the one +nearest the fan along the fan's facing axis - begins to translate in +x +and to roll ... Nothing else moves." It modelled it as a force through +the engine rather than a feature overwrite, because "the engine must +resolve the resulting contacts (that is what produces the cascade)". + +It also found something the hand-written ground-truth simulator does +not model. The wind is **occluded**: a staged blue on the fan's axis +shows roll exactly 0.000 until the toppling green reaches it, and "a +sub-threshold force would have produced a visible lean", so an upright +domino blocks the beam. `domino_fan/gt_simulator.py` sidesteps this by +applying wind only to whichever block is painted green - a role lookup, +not physics. + +And it rebuilt the vocabulary it had been denied: `_toppled`, +`_upright`, `_fan_on`, `_fan_off`, `_switch_on`, and `_bridges_gap` - +its own `InFront`, named for what it does. + +The test layout has a 0.294 m gap against the 0.196 m it practised on, +so the model generalized rather than memorizing one scene. ## oracle_solve.gif diff --git a/docs/envs/domino_fan/discovered_predicates.py b/docs/envs/domino_fan/discovered_predicates.py new file mode 100644 index 000000000..625c6c359 --- /dev/null +++ b/docs/envs/domino_fan/discovered_predicates.py @@ -0,0 +1,119 @@ +"""Learned predicates for pybullet_domino_fan. + +All numeric cutoffs are shared with simulator.py's PARAM_SPECS via the +pre-injected `params` view, so a refit moves rule and predicate together. + +Evidence for each cutoff (belief-sim sweeps at the demo layout, which +reproduces both recorded cascades to <= 1 cm on every body): + * bridge_max_gap 0.13 : centre-to-centre spacings 0.06 / 0.08 / 0.10 / + 0.12 / 0.14 all relay the cascade to the purple target; 0.16 stalls + (the struck domino only reaches roll 0.12). 0.13 sits inside the + working bucket with a clear margin to the 0.16 failure. + * bridge_max_lateral 0.035 : lateral offsets of 0.00 and 0.06 still + relay, 0.12 does not. 0.035 is deliberately tighter than the + empirical boundary (tighten, never widen). + * bridge_yaw_align 0.5 : |a.u| of 0.45 and 0.70 relay, 0.92 and 1.00 + (edge-on bridge) stall the chain and even jam the green start block + at roll ~0.5. + * toppled_roll 0.7 : recorded rolls are ~0 while standing and settle at + 1.44-1.57 once down; nothing ever rests between 0.2 and 1.4. +Recorded domino poses are BODY CENTRES (z = 0.475 = table 0.40 + half of +the 0.15 m height), so domino-domino spacing needs no anchor offset - the +recorded origin is the functional point. The fan, by contrast, is only +ever used through its `rot`-derived facing direction, never its origin +distance, so no fan anchor offset is needed either. +""" + + +_DEFAULTS = { + "bridge_max_gap": 0.13, + "bridge_max_lateral": 0.035, + "bridge_yaw_align": 0.5, + "toppled_roll": 0.7, + "upright_roll": 0.2, +} + + +def _p(name): + """Read a shared simulator ParamSpec, falling back to its declared + init_value when no fit has populated the params view yet (the + predicate-quality loader can run before the first fit).""" + try: + return float(params[name]) + except Exception: + return _DEFAULTS[name] + + +def _roll(s, d): + return abs(float(s.get(d, "roll"))) + + +def _toppled(s, objs, latent=None): + return _roll(s, objs[0]) >= _p("toppled_roll") + + +def _upright(s, objs, latent=None): + return _roll(s, objs[0]) < _p("upright_roll") + + +def _fan_on(s, objs, latent=None): + return float(s.get(objs[0], "is_on")) > 0.5 + + +def _fan_off(s, objs, latent=None): + return float(s.get(objs[0], "is_on")) <= 0.5 + + +def _switch_on(s, objs, latent=None): + return float(s.get(objs[0], "is_on")) > 0.5 + + +def _xy(s, o): + return np.array([float(s.get(o, "x")), float(s.get(o, "y"))]) + + +def _bridges_gap(s, objs, latent=None): + """`bridge` stands between `src` and `tgt` close enough, and squarely + enough, that a topple travelling src -> tgt relays through it.""" + bridge, src, tgt = objs + if bridge is src or bridge is tgt or src is tgt: + return False + if float(s.get(bridge, "is_held")) > 0.5: + return False + p_b, p_s, p_t = _xy(s, bridge), _xy(s, src), _xy(s, tgt) + span = p_t - p_s + span_len = float(np.linalg.norm(span)) + if span_len < 1e-6: + return False + u = span / span_len + perp = np.array([-u[1], u[0]]) + along = float((p_b - p_s) @ u) + # must sit strictly between the two, and split the span into two + # hops each short enough to carry the topple + if along <= 0.0 or along >= span_len: + return False + if float(np.linalg.norm(p_b - p_s)) > _p("bridge_max_gap"): + return False + if float(np.linalg.norm(p_t - p_b)) > _p("bridge_max_gap"): + return False + if abs(float((p_b - p_s) @ perp)) > _p("bridge_max_lateral"): + return False + # face the oncoming domino squarely: the width axis (cos yaw, sin yaw) + # must be near-perpendicular to the cascade direction + yaw = float(s.get(bridge, "yaw")) + axis = np.array([np.cos(yaw), np.sin(yaw)]) + if abs(float(axis @ u)) > _p("bridge_yaw_align"): + return False + return True + + +LEARNED_PREDICATES = [ + Predicate("DomToppled", [domino_type], _toppled), + Predicate("DomUpright", [domino_type], _upright), + Predicate("FanRunning", [fan_type], _fan_on), + Predicate("FanIdle", [fan_type], _fan_off), + Predicate("SwitchPressed", [switch_type], _switch_on), + Predicate("DomBridges", [domino_type, domino_type, domino_type], + _bridges_gap), +] + diff --git a/docs/envs/domino_fan/discovered_simulator.py b/docs/envs/domino_fan/discovered_simulator.py index 6fa01510c..665e10b4a 100644 --- a/docs/envs/domino_fan/discovered_simulator.py +++ b/docs/envs/domino_fan/discovered_simulator.py @@ -1,127 +1,180 @@ """Residual dynamics for pybullet_domino_fan. -DECISION RECORD -=============== -Evidence (2 successful interaction trajectories, task 0, reward 0.95 each): - -* Base sim carries: robot motion, grasping (Pick/Place), and ALL rigid-body - domino-on-domino cascade physics. In both trajectories the blue domino is - picked from (0.47, 1.275) and placed between the green (x=0.54) and the - purple (x=0.736); the recorded contact-driven part of the cascade - (green -> blue -> purple, rolls ramping to ~1.57 within 3-4 steps of each - contact) is ordinary rigid-body physics and needs no rule. - -* MISSING mechanism = the fan's wind. Exactly one step after fan_0.is_on - flips to 1 (t=98 -> 99 in traj 0, t=112 -> 113 in traj 1) the GREEN domino - - the one nearest the fan along the fan's facing axis - begins to translate - in +x and to roll (roll 0 -> 0.047 -> 0.126 -> 0.19 -> 0.29 -> 0.49 ...), - with an essentially identical profile in both trajectories. Nothing else - moves. The base sim replays this segment inert (confirmed by rollout probe - and by two earlier cycles that saw no topple at all with the fan on), so - this is diagnostic-ladder case 2: an exogenous influence the engine knows - nothing about. It is modelled with a constant `cmds.apply_force` gated on - `is_on`, NOT with a feature overwrite - the engine must resolve the - resulting contacts (that is what produces the cascade). - -* Wind is OCCLUDED, not range-limited-only. The placed blue domino sits - 0.30-0.32 m from the fan, directly on the fan's axis, and shows roll - EXACTLY 0.000 until the toppling green physically reaches it. A - sub-threshold force would have produced a visible lean, so the blue - receives no wind at all while the upright green stands in front of it. - The rule therefore applies wind only to a domino that has no upright - domino between it and the fan (lateral overlap < `wind_beam_halfwidth`), - plus a generous learnable `wind_range` cutoff. - -* Latent state: NONE needed. Every driver of the residual is observable - (fan.is_on, fan.rot, domino poses). `latent` is threaded but unused. - -* PHYSICAL_PARAMS: not declared (see rollout sweep evidence in the session); - the cascade geometry the base sim produces already matches the recorded - contact chain once the wind force exists. Re-examine only if a calibrated - wind still fails to propagate. +DECISION RECORD (cycle 3) +================================= +Observations (trajectories 0 and 1, both solved, reward 0.95): + * Both episodes: Pick blue domino_1 -> Place it between green (domino_0, + x=0.540) and purple target (domino_2, x=0.736) -> TurnFanOn -> cascade. + * The instant `fan_0.is_on` flips 0 -> 1 (traj0 step 99, traj1 step 88) the + GREEN start block begins to tip on the very next recorded state + (roll 0 -> 0.05 -> 0.13 -> 0.19 -> 0.30 -> 0.51 -> 0.89 -> 1.26 -> 1.57) + while sliding +x by ~6 cm. No robot contact is involved. + * The blue (x=0.638 / 0.662) and the purple (x=0.736) do NOT move while the + green is still upright; they only start rolling one step AFTER the body in + front of them has tipped into them (blue at green.roll~0.9, purple at + blue.roll~0.4). So the chain itself is ordinary contact physics. + * Previous-cycle journal: with fan_0.is_on = 1 and several Wait options, + the base sim moves NOTHING (green roll stays 1e-4). So the wind is an + exogenous influence the engine knows nothing about => diagnostic-ladder + case 2 => model it with physics COMMANDS (constant force gated on is_on), + not with feature overwrites and not with PHYSICAL_PARAMS. + +Modeling choices: + * ONE rule, `fan_wind`. While a fan is on it emits a constant world-frame + force along the fan's facing direction (cos(rot), sin(rot)) on the closest + UPRIGHT, un-held domino that lies inside a learned downwind corridor + (half-width + range). Only the closest one: in the data the second and + third dominoes are shielded by the first and never move under wind alone, + and options.py documents that "the fan cuts out the moment the start block + falls". Modelling only the nearest exposed body is both consistent with + the data and conservative for planning - the planner must bridge the gap + with blue dominoes rather than hoping the wind reaches across it. + * Everything downstream of the first tip (domino-domino collisions, sliding, + settling) is left to the base sim's rigid-body engine. No rule touches it. + * No latent state is needed: the wind's driver (`fan.is_on`) is observable + and the response is instantaneous (<1 step), so LATENT_INIT is empty. + * PHYSICAL_PARAMS: NOT declared. Decided from open-loop evidence, not from + small per-step residuals. With the wind rule riding, + `sim.residuals(rollout=True, sweep_params='all')` gives baseline SSE 2.96 + and every swept alternative is worse or flat: + lateral_friction 0.01/0.038/0.14/0.53/2.0 -> 73.9/69.6/68.3/1.81/69.3 + mass 0.005..1 -> 68.3/18.1/7.2/68.7/68.7 + restitution 0..0.9 -> 2.95/2.95/2.95/2.98/3.58 + rolling_friction 0/0.025/0.05/0.075/0.1 -> 3.00/70.6/70.6/70.6/65.8 + spinning_friction 0.01..2 -> 1.79/2.97/2.97/1.78/1.86 + Nothing clears the 3x consistency bar over the registry baseline (the best, + lateral_friction 0.53, is 1.6x and is essentially the baseline value), so + the shipped rigid-body physics is already calibrated for this data and + declaring a parameter would fit noise. + +Calibration + validation (belief probe, task 0): + * wind_force calibrated by replaying each recorded pre-fan scene + (`sim.reset(mods=...)` staging the blue at its recorded pre-fan pose) and + matching the settled poses. At 0.15-0.18 N every body lands within 1 cm + of the recording: traj0 (blue @0.6375) predicted g/b/p x = 0.598/0.722/ + 0.836 vs recorded 0.586/0.722/0.835, rolls 1.45/1.44/1.57 vs 1.46/1.44/ + 1.57; traj1 (blue @0.662) predicted 0.571/0.769/0.827 vs recorded + 0.571/0.767/0.827. 1.2 N (the first guess) shot the green 30 cm downrange + and produced a fantasy solve with no bridge at all - the calibration is + what makes the model refuse that. + * Sanity check in the other direction: with NO bridge the wind topples only + the green (it ends at x=0.636) and the purple stays upright -> goal not + reached. The model therefore reproduces the env's actual requirement that + a blue must bridge the 0.196 m green->purple gap, and 0.95 (one blue + consumed) is the best score physically available on this task. + * Cascade-geometry sweeps used to fit the predicate cutoffs in + predicates.py: centre spacings 0.06-0.14 relay, 0.16 stalls; lateral + offsets 0.00-0.06 relay, 0.12 does not; bridge yaw within ~+-0.77 rad of + square relays, edge-on (yaw 0.0/0.4) stalls and even jams the green. All + eight corners of the region DomBridges accepts were re-simulated and every + one topples the purple target. + * Known harness caveat this cycle: `evaluate_predicate_quality` loads and + scores predicates.py fine, but the run_python BeliefProbe still parses + sketches against the Holding-only allowlist, so learned-predicate subgoal + annotations are silently dropped there and `sim.refine` cannot be used as + the goal gate (it then places the blue anywhere and misses the goal). + Validation was therefore done with fully-parameterised `sim.run` plans + plus the corner sweeps above; the reference plan below runs end-to-end + with `Goal reached: True`. + +Reference plan (validated in the calibrated model, matches both recordings): + Pick(robot, domino_1)[0.05] + Place(robot)[0.638, 1.38653, 0.57, 1.5708] + TurnFanOn(robot, fan_0)[0.1, 0.11] + Wait(robot) x2-3 """ from typing import Any, Dict, List -import numpy as np +UPRIGHT_ROLL = 0.25 # |roll| below this counts as still standing -# ---------------------------------------------------------------- params ---- -PARAM_SPECS = [ - # Constant wind force (N) applied to the exposed domino while the fan is on. - ParamSpec("wind_force", 0.18, lo=0.179999, hi=0.180001), - # Reach of the wind measured along the fan's facing axis (m). - ParamSpec("wind_range", 0.60, lo=0.05, hi=1.5), - # Half-width of the wind beam, lateral to the facing axis (m). - ParamSpec("wind_beam_halfwidth", 0.06, lo=0.01, hi=0.30), - # |roll| above which a domino counts as toppled (no longer blocks wind, - # and, for predicates, counts as "down"). - ParamSpec("topple_roll", 0.60, lo=0.15, hi=1.40), - # Upright tolerance: |roll| below this means "still standing". - ParamSpec("upright_roll", 0.10, lo=0.01, hi=0.40), -] - -LATENT_INIT: Dict[str, Any] = {} +# Structural (NOT fitted) wind-corridor constants. The data contains exactly +# one body in the wind (the green start block, 0.22 m downwind, dead on the +# fan axis), so neither the corridor's length nor its width is identifiable - +# sim.fit reported contraction ~1 for both when they were ParamSpecs. Rather +# than let MCMC wander them to an arbitrary value, they are pinned to +# deliberately CONSERVATIVE values: just past the green block and about one +# domino width. Erring short is safe (the planner then has to bridge the gap +# with blue dominoes, which is what the data shows works); erring long would +# invent a wind that topples the target directly and produce plans that fail +# for real. +WIND_RANGE = 0.26 # m downwind reach of the jet +WIND_HALF_WIDTH = 0.09 # m half-width of the jet corridor -RESIDUAL_FEATURES = {"domino": ["x", "z", "roll"]} - -# ----------------------------------------------------------------- rules ---- -def _wind_axis(observation, fan): - rot = float(observation.get(fan, "rot")) +def _fan_dir(state, fan): + rot = float(state.get(fan, "rot")) return np.array([np.cos(rot), np.sin(rot)]) -def wind_rule(observation, latent, history, updates, params, cmds): - """Constant wind force on the domino at the FRONT of a running fan's beam. - - Conservative by construction: only the frontmost domino in the beam is - ever pushed (that is all the data shows moving), so every downstream - topple must be earned by a real contact in the engine. The force tapers - with cos(roll) - the projected frontal area of a tipping domino - which - is what stops it once the domino is on the table. - """ - del latent, history +def fan_wind(observation, latent, history, updates, params, cmds): + """Constant wind force from every switched-on fan on the first + exposed upright domino downwind of it.""" fans = [o for o in observation.data if o.type.name == "fan"] dominoes = [o for o in observation.data if o.type.name == "domino"] if not fans or not dominoes: return updates - for fan in fans: if float(observation.get(fan, "is_on")) <= 0.5: continue - axis = _wind_axis(observation, fan) - perp = np.array([-axis[1], axis[0]]) - origin = np.array([ - float(observation.get(fan, "x")), - float(observation.get(fan, "y")), - ]) - - best = None # (along, domino, roll) - for d in dominoes: - if float(observation.get(d, "is_held")) > 0.5: + d = _fan_dir(observation, fan) + perp = np.array([-d[1], d[0]]) + origin = np.array([float(observation.get(fan, "x")), + float(observation.get(fan, "y"))]) + best, best_along = None, None + for dom in dominoes: + if float(observation.get(dom, "is_held")) > 0.5: continue - v = np.array([ - float(observation.get(d, "x")), - float(observation.get(d, "y")), - ]) - origin - along = float(v @ axis) - lateral = abs(float(v @ perp)) - if along <= 0.0 or along > params["wind_range"]: + if abs(float(observation.get(dom, "roll"))) > UPRIGHT_ROLL: + continue # already toppling: no longer a sail + rel = np.array([float(observation.get(dom, "x")), + float(observation.get(dom, "y"))]) - origin + along = float(rel @ d) + lateral = abs(float(rel @ perp)) + if along <= 0.0 or along > WIND_RANGE: continue - if lateral > params["wind_beam_halfwidth"]: + if lateral > WIND_HALF_WIDTH: continue - roll = abs(float(observation.get(d, "roll"))) - if best is None or along < best[0]: - best = (along, d, roll) - if best is None: - continue - _, d, roll = best - taper = max(0.0, float(np.cos(roll))) - f = params["wind_force"] * taper - if f <= 0.0: - continue - cmds.apply_force(d, (float(axis[0] * f), float(axis[1] * f), 0.0)) + if best_along is None or along < best_along: + best, best_along = dom, along + if best is not None: + f = params["wind_force"] + cmds.apply_force(best, (float(f * d[0]), float(f * d[1]), 0.0)) return updates -RESIDUAL_RULES: List[Any] = [wind_rule] +RESIDUAL_RULES = [fan_wind] + +PARAM_SPECS = [ + # Newtons, applied at the body COM (0.075 m above the table) every physics + # substep while the fan is on. Static topple threshold for a 0.1 kg, + # 0.15 x 0.014 m domino is ~0.09 N, so the box brackets "just tips it" to + # "shoves it hard"; 0.15 already reproduced both recorded cascades to + # ~1 cm on every body (see decision record). + ParamSpec("wind_force", 0.175, lo=0.05, hi=0.45), + # Predicate-only geometry (no SSE signal - it never enters a rule, so it + # stays at init_value): the largest centre-to-centre spacing at which a + # toppling domino still reliably knocks over the next one. Bodies are + # 0.15 m tall and ~0.014 m deep, so contact is geometrically possible out + # to ~0.16 m; 0.13 keeps the strike high on the neighbour's face. + ParamSpec("bridge_max_gap", 0.13, lo=0.05, hi=0.17), + # Half-width of the corridor a bridging domino must sit in, measured + # perpendicular to the source->target line. + ParamSpec("bridge_max_lateral", 0.035, lo=0.005, hi=0.08), + # |roll| above which a domino counts as toppled / below which it counts + # as still standing. + # |a . u| cap, where a = (cos yaw, sin yaw) is the domino's width axis + # and u the cascade direction: how squarely the bridge domino must face + # the oncoming domino. Probe sweep at the demo layout: yaw giving + # |a.u| = 0.45 / 0.70 relay fine, 0.92 / 1.00 (edge-on) stall the chain, + # so 0.5 sits well inside the working bucket. + ParamSpec("bridge_yaw_align", 0.5, lo=0.1, hi=0.85), + ParamSpec("toppled_roll", 0.7, lo=0.3, hi=1.4), + ParamSpec("upright_roll", 0.2, lo=0.05, hi=0.4), +] + +LATENT_INIT: Dict[str, Any] = {} + +# The wind moves the first domino's pose; the engine carries it and the rest +# of the chain. These are scored against observations, never overwritten. +RESIDUAL_FEATURES = {"domino": ["x", "y", "z", "roll"]} From 55bc305d240190966ca73b7a60078a77940bfda1 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 10:02:09 -0400 Subject: [PATCH 40/48] ignore the per-rung configs run_rung.sh generates --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 7b8ba8dc1..93266140e 100644 --- a/.gitignore +++ b/.gitignore @@ -42,3 +42,7 @@ predicators/datasets/vlm_input_data_prompts/vision_api/response.txt paper/ .venv/ + +# Generated by scripts/domino_fan/run_rung.sh, one per env+rung. +scripts/configs/predicatorv3/_*_rung*.yaml +scripts/configs/predicatorv3/_rung*.yaml From 050c0fef4f62cf52c2133622ddd3089bd5a51c32 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 10:02:43 -0400 Subject: [PATCH 41/48] domino_fan: SEED=N to repeat a rung A rung's result on one seed is an anecdote. The agent's exploration is not deterministic - two runs of the same config took 21 and 83 minutes to reach the same phase - so a repeat on another seed is the cheapest evidence that a number is the method rather than the draw. Runs land in seed/ and appear as separate dashboard rows. --- scripts/domino_fan/run_rung.sh | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index 03fbf2f20..16e488451 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -12,6 +12,10 @@ # # scripts/domino_fan/run_rung.sh --declare 1 # +# SEED=1 repeats a rung on another seed, for a second opinion: +# +# SEED=1 scripts/domino_fan/run_rung.sh 4 +# # Each rung gives the agent less and asks it to recover more: # 1 GT simulator + GT predicates, process planner plans. # 2 GT simulator + GT predicates, the AGENT plans. @@ -35,6 +39,12 @@ if [ "${1:-}" = "--declare" ]; then ENV_KEY="domino_declare" shift fi + +# Which random seed to run. A rung's result on one seed is an anecdote: +# the agent's exploration is not deterministic, so a repeat with a +# different seed is the cheapest evidence that a result is the method +# rather than the draw. Runs land in seed/ and show as separate rows. +SEED="${SEED:-0}" [ $# -ge 1 ] || usage run_one() { @@ -55,11 +65,11 @@ run_one() { # experiment_id is built by cluster_utils as "-", # so it is domino_fan-; the dashboard maps that back to a rung. - local cfg="scripts/configs/predicatorv3/_${ENV_KEY}_rung${rung}.yaml" + local cfg="scripts/configs/predicatorv3/_${ENV_KEY}_rung${rung}_s${SEED}.yaml" # Generated, not hand-edited: every arm parked except this one. - $PY - "$rung" "$arm" "$ENV_KEY" > "$cfg" <<'PYEOF' + $PY - "$rung" "$arm" "$ENV_KEY" "$SEED" > "$cfg" <<'PYEOF' import sys -rung, arm, env_key = sys.argv[1], sys.argv[2], sys.argv[3] +rung, arm, env_key, seed = sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4] arms = ["oracle", "agent_model_based_planning", "agent_param_learning", "agent_po_predicate_invention_al"] lines = [ @@ -70,6 +80,8 @@ lines = [ " - common.yaml", " - envs/all.yaml", " - approaches/all.yaml", + f"START_SEED: {seed}", + "NUM_SEEDS: 1", "ENVS:", f" {env_key}:", " SKIP: False", @@ -123,10 +135,11 @@ print("\n".join(lines)) PYEOF echo "==========================================================" - echo " RUNG $rung -> $arm" + echo " RUNG $rung -> $arm (seed $SEED)" echo " logs/*/${ENV_KEY}-$arm/ (rung $rung: $exp)" echo "==========================================================" - PYTHONHASHSEED=0 $PY scripts/local/launch_simp.py -c "predicatorv3/_${ENV_KEY}_rung${rung}.yaml" + PYTHONHASHSEED=0 $PY scripts/local/launch_simp.py \ + -c "predicatorv3/_${ENV_KEY}_rung${rung}_s${SEED}.yaml" } if [ "$1" = "all" ]; then From 177d4c9f4bc1e95b436338c7385f6427918898af Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 17:09:10 -0400 Subject: [PATCH 42/48] domino_blow: place a block so the wind carries it into a goal region A new env, pybullet_domino_blow: fan, goal patch, staged block, left to right. The robot picks the block, puts it down between the fan and the patch, and declares finished; the fan blows for a bounded number of steps and the block slides. It scores if the block comes to rest inside the patch. This is the task pybullet_domino_fan could not be. There the wind tips a standing domino in about two steps, so every force above threshold produces the same observation and wind_force is unfittable - 1.5 N and 2.0 N give identical trajectories. Here the wind pushes through the block's CENTRE OF MASS, so it slides and the distance travelled is a continuous, monotone function of the force. Same reason pybullet_fan can fit this parameter and the cascade env cannot: what the wind is pushing decides whether its strength leaves a trace. Two details carry the design. The gust is FINITE, because a wind that blows until the horizon puts every sufficient force against the same far wall - the saturation this env exists to avoid. And the goal is a bounded REGION, which answers the question raised in the design meeting: placing the block as close to the fan as possible, the obvious way to avoid learning anything, overshoots the far edge. In place so far: the goal-region component and its InGoal predicate, the env composing it, and the wind wired through the centre of mass. Still to come: the task generator for this layout, the GT models, and the residual simulator whose wind_force is the thing to be fitted. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../components/goal_region_component.py | 195 ++++++++++++++++++ predicators/envs/pybullet_domino/env.py | 113 +++++++++- predicators/settings.py | 14 ++ 3 files changed, 319 insertions(+), 3 deletions(-) create mode 100644 predicators/envs/pybullet_domino/components/goal_region_component.py diff --git a/predicators/envs/pybullet_domino/components/goal_region_component.py b/predicators/envs/pybullet_domino/components/goal_region_component.py new file mode 100644 index 000000000..27c6d73f4 --- /dev/null +++ b/predicators/envs/pybullet_domino/components/goal_region_component.py @@ -0,0 +1,195 @@ +"""Goal region for the wind-placement task. + +A flat patch on the table the wind has to deliver a domino INTO. It is +scenery, not an obstacle: zero mass, collisions disabled, drawn only so +a person watching the video can see what the robot is aiming at. + +The region exists so the task cannot be solved by pushing as hard as +possible. Emily's question in the design meeting was whether a robot +could simply place the block as close to the fan as it can every time; +with a target POINT it could, and with a bounded region it cannot - +place too near the fan and the wind carries the block past the far +edge, too far and it never arrives. Only a band of placements works, +and finding that band means knowing how far this wind pushes this +block, which is the parameter the task exists to make learnable. +""" + +from typing import Any, ClassVar, Dict, List, Optional, Sequence, Set, Tuple + +import numpy as np +import pybullet as p + +from predicators.envs.pybullet_domino.components.base_component import \ + DominoEnvComponent +from predicators.pybullet_helpers.objects import create_pybullet_block +from predicators.structs import Object, Predicate, State, Type + + +class GoalRegionComponent(DominoEnvComponent): + """One rectangular goal patch, and the predicate for being in it.""" + + # A patch big enough to be hittable and small enough that "as hard + # as possible" overshoots it. 6 cm of slack along the wind axis + # against a slide of tens of centimetres. + region_half_x: ClassVar[float] = 0.06 + region_half_y: ClassVar[float] = 0.09 + region_thickness: ClassVar[float] = 0.001 + region_color: ClassVar[Tuple[float, float, float, + float]] = (0.2, 0.85, 0.35, 0.55) + + def __init__(self, + workspace_bounds: Optional[Dict[str, float]] = None, + table_height: float = 0.4, + domino_type: Optional[Type] = None) -> None: + super().__init__() + self.table_height = table_height + self._domino_type = domino_type + if workspace_bounds is None: + workspace_bounds = { + "x_lb": 0.4, + "x_ub": 1.1, + "y_lb": 1.1, + "y_ub": 1.6 + } + self.x_lb = workspace_bounds["x_lb"] + self.x_ub = workspace_bounds["x_ub"] + self.y_lb = workspace_bounds["y_lb"] + self.y_ub = workspace_bounds["y_ub"] + + # half_x / half_y are features rather than constants so an agent + # reading the state can see how much slack it has, and so a task + # generator could vary the difficulty without a code change. + self._region_type = Type( + "region", ["x", "y", "z", "half_x", "half_y"], + sim_features=["id"]) + self._region = Object("goal_region", self._region_type) + self._region_id: Optional[int] = None + self._region_xy: Tuple[float, float] = (0.0, 0.0) + + self._InGoal = Predicate("InGoal", + [self._domino_type, self._region_type] + if self._domino_type is not None else + [self._region_type], self._InGoal_holds) + + # -- component interface ------------------------------------------ + + def get_types(self) -> Set[Type]: + return {self._region_type} + + def get_predicates(self) -> Set[Predicate]: + return {self._InGoal} + + def get_goal_predicates(self) -> Set[Predicate]: + return {self._InGoal} + + def get_objects(self) -> List[Object]: + return [self._region] + + def initialize_pybullet(self, physics_client_id: int) -> Dict[str, Any]: + """A thin coloured plate lying on the table. + + Mass 0 and collisions off: the region must not deflect the very + block it is measuring, and a lip of even a millimetre would. + """ + self._physics_client_id = physics_client_id + region_id = create_pybullet_block( + color=self.region_color, + half_extents=(self.region_half_x, self.region_half_y, + self.region_thickness), + mass=0.0, + friction=0.5, + position=(0.0, 0.0, self.table_height + self.region_thickness), + orientation=(0.0, 0.0, 0.0, 1.0), + physics_client_id=physics_client_id) + p.setCollisionFilterGroupMask(region_id, + -1, + 0, + 0, + physicsClientId=physics_client_id) + return {"region_id": region_id} + + def store_pybullet_bodies(self, pybullet_bodies: Dict[str, Any]) -> None: + self._region_id = pybullet_bodies["region_id"] + self._region.id = self._region_id + + def reset_state(self, state: State) -> None: + x = float(state.get(self._region, "x")) + y = float(state.get(self._region, "y")) + self._region_xy = (x, y) + if self._region_id is not None: + p.resetBasePositionAndOrientation( + self._region_id, + (x, y, self.table_height + self.region_thickness), + (0.0, 0.0, 0.0, 1.0), + physicsClientId=self._physics_client_id) + + def extract_feature(self, obj: Object, feature: str) -> Optional[float]: + if obj.type != self._region_type: + return None + if feature == "x": + return self._region_xy[0] + if feature == "y": + return self._region_xy[1] + if feature == "z": + return self.table_height + self.region_thickness + if feature == "half_x": + return self.region_half_x + if feature == "half_y": + return self.region_half_y + return None + + def get_init_dict_entries( + self, rng: np.random.Generator) -> Dict[Object, Dict[str, Any]]: + """Placed by the task generator, which knows where the fan is.""" + del rng + x, y = self._region_xy + return { + self._region: { + "x": x, + "y": y, + "z": self.table_height + self.region_thickness, + "half_x": self.region_half_x, + "half_y": self.region_half_y, + } + } + + def get_object_ids_for_held_check(self) -> List[int]: + return [] + + # -- placement, used by the task generator ------------------------ + + def set_region_xy(self, x: float, y: float) -> None: + """Put the region where the generator decided it goes.""" + self._region_xy = (x, y) + + # -- predicate ---------------------------------------------------- + + def _InGoal_holds(self, state: State, objects: Sequence[Object]) -> bool: + """The domino's centre lies inside the patch. + + Centre rather than footprint overlap: a block half in and half + out is a coin toss on the exact contact solve, and the reward + should not turn on which millimetre the solver settled at. + """ + if len(objects) != 2: + return False + domino, region = objects + dx = abs(float(state.get(domino, "x")) - float(state.get(region, "x"))) + dy = abs(float(state.get(domino, "y")) - float(state.get(region, "y"))) + return (dx <= float(state.get(region, "half_x")) + and dy <= float(state.get(region, "half_y"))) + + @property + def region(self) -> Object: + """The goal patch object.""" + return self._region + + @property + def region_type(self) -> Type: + """Type of the goal patch.""" + return self._region_type + + @property + def InGoal(self) -> Predicate: + """True when a domino's centre is inside the patch.""" + return self._InGoal diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index 71266e305..b56e20dd7 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -18,6 +18,8 @@ DominoEnvComponent from predicators.envs.pybullet_domino.components.domino_component import \ DominoComponent +from predicators.envs.pybullet_domino.components.goal_region_component \ + import GoalRegionComponent from predicators.envs.pybullet_domino.components.fan_component import \ FanComponent from predicators.envs.pybullet_domino.components.grid_component import \ @@ -1054,9 +1056,15 @@ def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: # along side 0. The other three fans would be distractors the # planner still has to ground, and being opposed they cancel. fan_comp = self._make_fan_component(bounds) - super().__init__(components=[domino_comp, fan_comp], - use_gui=use_gui, - **kwargs) + components = [domino_comp, fan_comp] + components += self._extra_components(bounds, domino_comp) + super().__init__(components=components, use_gui=use_gui, **kwargs) + + def _extra_components(self, bounds: Dict[str, float], + domino_comp: DominoComponent) -> List[Any]: + """Components beyond the dominoes and the fan. None by default.""" + del bounds, domino_comp + return [] def _make_fan_component(self, bounds: Dict[str, float]) -> FanComponent: """The fan bank for this env. Overridden where the switch is @@ -1145,6 +1153,105 @@ def _domain_specific_step(self) -> None: super()._domain_specific_step() +class PyBulletDominoBlowEnv(PyBulletDominoDeclareEnv): + """Place a block so the wind carries it INTO a goal region. + + The scene reads left to right: fan, goal patch, staged block. The + robot picks the block up, puts it down somewhere between the fan + and the patch, and declares finished; the fan then blows for a + bounded number of steps and the block slides. It scores if the + block comes to rest inside the patch. + + This task exists because of what pybullet_domino_fan could NOT + teach. There the wind tips a standing domino in about two steps, + so every force above the tipping threshold produces the same + observation and ``wind_force`` is unfittable - 1.5 N and 2.0 N give + identical trajectories (measured by + scripts/domino_debug/probe_wind_identifiability.py). Here the wind + pushes through the block's CENTRE OF MASS, so it slides rather than + tips and the distance it travels is a continuous, monotone function + of the force. That is the same reason pybullet_fan can fit this + parameter and the domino env cannot: what the wind is pushing + decides whether its strength leaves a trace. + + And the goal is a bounded REGION, not a point, which is what stops + the degenerate policy. Placing the block as close to the fan as + possible - the obvious way to avoid learning anything - overshoots + the far edge. Placing it safely far never arrives. Only a band of + placements works, and its position depends on how hard this fan + blows, so the robot cannot reach the goal without having learned + that. + """ + + def __init__(self, use_gui: bool = False, **kwargs: Any) -> None: + self._goal_region_component: Optional[GoalRegionComponent] = None + super().__init__(use_gui=use_gui, **kwargs) + self._wind_steps_left: int = 0 + + def _extra_components(self, bounds: Dict[str, float], + domino_comp: DominoComponent) -> List[Any]: + """The patch the block has to end up in.""" + self._goal_region_component = GoalRegionComponent( + workspace_bounds=bounds, + table_height=self.table_height, + domino_type=domino_comp.domino_type) + return [self._goal_region_component] + + @classmethod + def get_name(cls) -> str: + return "pybullet_domino_blow" + + def _set_domain_specific_state(self, state: State) -> None: + super()._set_domain_specific_state(state) + # A fresh episode gets a fresh gust budget, and the wind aims at + # the movable block through its centre so the block slides. + self._wind_steps_left = CFG.domino_blow_wind_steps + self._wire_blow_target(state) + + def _wire_blow_target(self, state: State) -> None: + """Aim the fan at the block it is supposed to move. + + z_offset 0.0 is the whole difference from the cascade envs: a + force through the centre of mass is pure translation. In + pybullet_domino_fan that same setting was a BUG - the start + block slid a metre and a half into the target without ever + tipping, "solving" a bridge task with no bridge - and here it + is the mechanism. + """ + if self._fan_component is None or self._domino_component is None: + return + movable = [ + obj for obj in state + if obj.type == self._domino_component.domino_type + # pylint: disable-next=protected-access + and self._domino_component._MovableBlock_holds(state, [obj]) + ] + if not movable: + return + block = movable[0] + self._fan_component.set_lateral_alignment(float( + state.get(block, "y"))) + self._fan_component.set_wind_target( + block.id, + z_offset=0.0, + stop_when_toppled=False, + force=CFG.domino_blow_wind_force) + + def _domain_specific_step(self) -> None: + super()._domain_specific_step() + # The gust is finite. Without a budget the block is pushed until + # the episode horizon and its resting place says nothing about + # the force - every force large enough to move it at all ends up + # against the far wall, which is the saturation this env was + # built to avoid. + if self._fan_component is None: + return + if self._fan_component.any_fan_on() and self._wind_steps_left > 0: + self._wind_steps_left -= 1 + if self._wind_steps_left == 0: + self._fan_component.set_fans_on(False) + + class PyBulletDominoFanRampEnv(PyBulletDominoComposedEnv): """Domino + fan + ball + ramp environment class.""" diff --git a/predicators/settings.py b/predicators/settings.py index 943552db4..27c1d4149 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -897,6 +897,20 @@ class GlobalSettings: # against the centre-of-mass push and the never-ending wind, and no # longer holds. domino_fan_wind_force = 1.5 + + # Wind force for the blow-to-goal task (N). Applied at the block's + # CENTRE OF MASS, so the block slides instead of tipping - the whole + # point of that task is that the distance travelled is a continuous, + # monotone function of this number, which is what makes it fittable + # at all. In pybullet_domino_fan the same parameter is NOT fittable, + # because there the wind tips a block in about two steps and every + # force above threshold looks the same (see + # scripts/domino_debug/probe_wind_identifiability.py). + domino_blow_wind_force = 0.35 + # Steps the fan blows for once the robot has declared. Bounded so + # the block travels a finite, repeatable distance rather than being + # pushed until the episode ends. + domino_blow_wind_steps = 60 # Sides carrying a fan + switch in pybullet_domino_fan, in order # left, right, down, up. One is the point of the task: the robot has # a single switch to find and press. Four is the ball task's layout From 61ed47fb697e3cd2ed614ed1b754869f8706ccbb Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 17:23:35 -0400 Subject: [PATCH 43/48] domino_blow: the task generator, the ground-truth model, and the config Layout, verified: fan x=0.320, goal patch x=0.684 +/-0.04, block staged at x=0.864. Fan left, patch middle, block right - and the block starts DOWNWIND of the patch, so leaving it where it is can never work. The process model is four processes and no grid, because this is a different task rather than the cascade with pieces switched off: PickBlock -> Holding(block) PlaceUpwind -> HandEmpty, ReadyToBlow(block, region) DeclareFinished -> FanOn(fan) WindCarriesToGoal -> InGoal(block, region) [exogenous] The last one is the shape of the whole task: the robot never carries the block into the goal, it arranges the world so the wind will and then says it is done. ReadyToBlow is an ORACLE HELPER predicate, never one of the env's, so an agent approach cannot read the slide distance off the state. Knowing how far this gust carries this block is the content of the task, and the oracle's advantage is that it is told. Calibrated by measurement: at 2.5 N over 60 steps the block slides 11.5 cm, and the response is steep and monotone - 1.8 N -> 5.8 cm, 2.2 -> 8.8, 2.8 -> 14.6, 3.2 -> 19.3. A quadratic fit reproduces those to under a millimetre. Against a 4 cm goal half-width the robot has to know the force to about a sixth, which is the identifiability domino_fan never had. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../components/goal_region_component.py | 6 +- .../task_generators/domino_task_generator.py | 91 ++++++++++++ .../ground_truth_models/domino/options.py | 14 +- .../ground_truth_models/domino/predicates.py | 61 ++++++++- .../ground_truth_models/domino/processes.py | 129 +++++++++++++++++- .../ground_truth_models/domino/types.py | 3 +- predicators/settings.py | 8 +- scripts/configs/predicatorv3/envs/all.yaml | 41 ++++++ scripts/domino_fan/run_rung.sh | 8 ++ 9 files changed, 347 insertions(+), 14 deletions(-) diff --git a/predicators/envs/pybullet_domino/components/goal_region_component.py b/predicators/envs/pybullet_domino/components/goal_region_component.py index 27c6d73f4..158ca343d 100644 --- a/predicators/envs/pybullet_domino/components/goal_region_component.py +++ b/predicators/envs/pybullet_domino/components/goal_region_component.py @@ -31,7 +31,11 @@ class GoalRegionComponent(DominoEnvComponent): # A patch big enough to be hittable and small enough that "as hard # as possible" overshoots it. 6 cm of slack along the wind axis # against a slide of tens of centimetres. - region_half_x: ClassVar[float] = 0.06 + # Half-width along the wind axis. 4 cm against an 11.6 cm slide + # means the robot has to know how far this gust carries the block to + # within about a quarter - loose enough to be learnable from a + # handful of episodes, tight enough that a coarse guess misses. + region_half_x: ClassVar[float] = 0.04 region_half_y: ClassVar[float] = 0.09 region_thickness: ClassVar[float] = 0.001 region_color: ClassVar[Tuple[float, float, float, diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index 90e7b9148..82a88fa70 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -139,6 +139,8 @@ def _generate_single_task( straight-only. Ignored on the min-block path, which fills its own quota from the same ratio. """ + if CFG.env == "pybullet_domino_blow": + return self._generate_blow_task(task_idx, rng) if CFG.domino_min_block_tasks: return self._generate_min_block_task(task_idx, rng) @@ -325,6 +327,95 @@ def _generate_single_task( goal_nl=goal_nl, evaluator=evaluator) + def _generate_blow_task( + self, task_idx: int, + rng: np.random.Generator) -> Optional[EnvironmentTask]: + """Place a block so the wind carries it into the goal patch. + + The scene is one line along the fan's axis: fan (off-table, at + low x, blowing +x), then the goal patch, then the block on its + staging spot downwind of the patch. The robot has to pick the + block up and put it down UPWIND of the patch, far enough back + that the gust delivers it into the patch rather than past it. + + The patch is placed first and the staging spot derived from it, + so the block never starts inside its own goal - which would make + the task solvable by doing nothing at all. + """ + dominos = self.domino.dominos + if not dominos or self._goal_region is None: + return None + x_lb, x_ub = self.domino.domino_x_lb, self.domino.domino_x_ub + y_lb, y_ub = self.domino.domino_y_lb, self.domino.domino_y_ub + + # One lane, chosen away from the workspace edges so both the + # placement band and the staging spot stay reachable. + lane_y = float(rng.uniform(y_lb + 0.05, y_ub - 0.05)) + # The patch sits in the middle third of the run, leaving room + # upwind for the placement band and downwind for staging. + goal_x = float( + rng.uniform(x_lb + 0.30 * (x_ub - x_lb), + x_lb + 0.55 * (x_ub - x_lb))) + self._goal_region.set_region_xy(goal_x, lane_y) + + # The block starts downwind of the patch: the wind blows +x, so + # from here the gust alone can never deliver it. Only a pick and + # a place upwind can. + stage_x = min(x_ub - 0.02, goal_x + 0.18) + if stage_x <= goal_x + 0.10: + return None + + obj_dict: Dict[Object, Dict[str, Any]] = {} + # rotation 0 makes the block face along +y; the wind is axial and + # pushes through the centre of mass, so facing does not matter + # here the way it does for a topple. + obj_dict[dominos[0]] = self.domino.place_domino(0, + stage_x, + lane_y, + 0.0, + rng=rng, + task_idx=task_idx) + # Every other domino body this env owns is parked out of view: + # the task is about ONE block, and spare bodies on the table + # would be distractors the planner still has to ground. + ox, oy = self.domino.out_of_view_xy + for i in range(1, len(dominos)): + obj_dict[dominos[i]] = self.domino.place_domino(i, + ox + 0.05 * i, + oy, + 0.0, + rng=rng, + task_idx=task_idx) + + init_dict: Dict[Object, Dict[str, Any]] = { + self.robot: self.robot_init_state.copy() + } + init_dict.update(obj_dict) + for component in self.additional_components: + if hasattr(component, "get_init_dict_entries"): + init_dict.update(component.get_init_dict_entries(rng)) + init_state = utils.create_state_from_dict(init_dict) + + goal_atoms = { + GroundAtom(self._goal_region.InGoal, + [dominos[0], self._goal_region.region]) + } + goal_nl = ( + "Pick up the block and put it down so that when you declare " + "you have finished, it ends up inside the green goal region. " + "The block starts downwind of the region, so leaving it where " + "it is cannot work. Declaring finished is the only thing that " + "starts the fan, and the fan blows for a limited time.") + return EnvironmentTask(init_state, goal_atoms, goal_nl=goal_nl) + + @property + def _goal_region(self) -> Any: + """The GoalRegionComponent, if this env has one.""" + for component in self.additional_components: + if type(component).__name__ == "GoalRegionComponent": + return component + return None + def _generate_min_block_task( self, task_idx: int, rng: np.random.Generator) -> Optional[EnvironmentTask]: diff --git a/predicators/ground_truth_models/domino/options.py b/predicators/ground_truth_models/domino/options.py index 5a111eb0a..9f5c7887f 100644 --- a/predicators/ground_truth_models/domino/options.py +++ b/predicators/ground_truth_models/domino/options.py @@ -47,9 +47,18 @@ def _skill_robot_env_cls(env_name: str) -> TypingType[PyBulletEnv]: # too, it just does not say so in its name. The agent in # run_20260831_122006 noticed before I did, and padded its plan with # extra Waits to compensate. +# Envs where the robot starts the fan by DECLARING it has finished +# rather than by pressing a switch. Named rather than tested by suffix, +# for the same reason as _WIND_STARTED_ENVS below. +_DECLARE_TRIGGER_ENVS = frozenset({ + "pybullet_domino_declare", + "pybullet_domino_blow", +}) + _WIND_STARTED_ENVS = frozenset({ "pybullet_domino_fan", "pybullet_domino_declare", + "pybullet_domino_blow", }) @@ -74,7 +83,8 @@ def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", "pybullet_domino_real_geometry", "pybullet_domino_fan", - "pybullet_domino_declare" + "pybullet_domino_declare", + "pybullet_domino_blow" } @classmethod @@ -142,7 +152,7 @@ def _get_options_skill_factories( # finished building - no contact, nothing to reach around, and # for a learner nothing mechanical to credit the wind to. if "switch" in types: - if CFG.env == "pybullet_domino_declare": + if CFG.env in _DECLARE_TRIGGER_ENVS: options.add( create_declare_option("DeclareFinished", cfg, robot_type)) else: diff --git a/predicators/ground_truth_models/domino/predicates.py b/predicators/ground_truth_models/domino/predicates.py index 276f916fa..9cb6ca473 100644 --- a/predicators/ground_truth_models/domino/predicates.py +++ b/predicators/ground_truth_models/domino/predicates.py @@ -6,10 +6,11 @@ single source of truth. """ -from typing import Dict, Set +from typing import Dict, Sequence, Set from predicators.ground_truth_models import GroundTruthPredicateFactory -from predicators.structs import Predicate, Type +from predicators.settings import CFG +from predicators.structs import Object, Predicate, State, Type class PyBulletDominoGroundTruthPredicateFactory(GroundTruthPredicateFactory): @@ -20,7 +21,8 @@ def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", "pybullet_domino_real_geometry", "pybullet_domino_fan", - "pybullet_domino_declare" + "pybullet_domino_declare", + "pybullet_domino_blow" } @classmethod @@ -32,8 +34,59 @@ def get_helper_predicates(cls, env_name: str, grid predicates. Only oracle / process-planning approaches consume these helpers; agent approaches run grid-free. """ - del env_name # unused + if env_name == "pybullet_domino_blow": + return _blow_helper_predicates(types) from predicators.envs.pybullet_domino.components.grid_component import \ GridComponent # pylint: disable=import-outside-toplevel return GridComponent(domino_type=types["domino"]).get_predicates() + + +# ── Blow task: the one thing the oracle knows and a learner must not ── + + +def _blow_slide_distance() -> float: + """How far this gust carries the block, in metres. + + A ground-truth constant, fitted from the env's own wind force by the + curve measured in settings.py (1.8 N -> 5.8 cm, 2.5 -> 11.5, 3.2 -> + 19.3): slide grows steeply and monotonically with force, which is + the property that makes this task's parameter learnable at all. + + This lives in the ORACLE's helper predicates, never in the env's, so + an agent approach cannot read it off the state. Knowing it is the + whole content of the task. + """ + # A quadratic least-squares fit to the measured curve, accurate to + # under a millimetre from 1.8 to 3.2 N (5.75 / 8.79 / 11.53 / 14.61 + # / 19.33 cm). Cheaper and clearer than shipping a table, and it + # extrapolates sensibly for a task generator that varies the force. + force = CFG.domino_blow_wind_force + return max(0.0, 0.02081 * force * force - 0.00703 * force + 0.00273) + + +def _blow_helper_predicates(types: Dict[str, Type]) -> Set[Predicate]: + """``ReadyToBlow``: the block is where the gust will deliver it. + + The oracle plans Place -> DeclareFinished -> Wait, and this is the + predicate that makes the Place worth doing: it is true exactly when + the block sits one slide-length upwind of the goal patch, within the + patch's own tolerance. The wind process then turns it into InGoal. + """ + domino_type = types["domino"] + region_type = types["region"] + + def _ready_holds(state: State, objects: Sequence[Object]) -> bool: + domino, region = objects + if state.get(domino, "is_held") > 0.5: + return False + # The fan blows +x, so the block must start upwind by one slide. + want_x = float(state.get(region, "x")) - _blow_slide_distance() + dx = abs(float(state.get(domino, "x")) - want_x) + dy = abs(float(state.get(domino, "y")) - float(state.get(region, "y"))) + return (dx <= float(state.get(region, "half_x")) + and dy <= float(state.get(region, "half_y"))) + + return { + Predicate("ReadyToBlow", [domino_type, region_type], _ready_holds) + } diff --git a/predicators/ground_truth_models/domino/processes.py b/predicators/ground_truth_models/domino/processes.py index 5a3e6419b..da567cfca 100644 --- a/predicators/ground_truth_models/domino/processes.py +++ b/predicators/ground_truth_models/domino/processes.py @@ -201,7 +201,8 @@ def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", "pybullet_domino_real_geometry", "pybullet_domino_fan", - "pybullet_domino_declare" + "pybullet_domino_declare", + "pybullet_domino_blow" } @classmethod @@ -210,7 +211,11 @@ def get_processes( Type], predicates: Dict[str, Predicate], options: Dict[str, ParameterizedOption]) -> Set[CausalProcess]: - del env_name # unused + if env_name == "pybullet_domino_blow": + # A different task, so a different model rather than the + # cascade one with pieces disabled: no chain, no topple, no + # grid. One block, one gust, one patch to land it in. + return _get_blow_processes(types, predicates, options) # These processes are defined over the grid (loc/angle/direction). # Only oracle / process-planning approaches request them, and they do @@ -478,7 +483,8 @@ def _get_fan_processes( # what the process is grounded on. robot = Variable("?robot", robot_type) fan = Variable("?fan", fan_type) - if CFG.env == "pybullet_domino_declare": + if CFG.env in ("pybullet_domino_declare", + "pybullet_domino_blow"): # The robot announces it has finished building and the fan # starts. The option takes only the robot -- there is # nothing to reach for -- so the fan appears in the @@ -789,7 +795,8 @@ def get_env_names(cls) -> Set[str]: return { "pybullet_domino_grid", "pybullet_domino", "pybullet_domino_real", "pybullet_domino_real_geometry", "pybullet_domino_fan", - "pybullet_domino_declare" + "pybullet_domino_declare", + "pybullet_domino_blow" } @classmethod @@ -800,3 +807,117 @@ def get_samplers(cls, env_name: str) -> Dict[str, ParameterizedSampler]: "Push": _push_option_sampler, "Place": _place_option_sampler, } + + +# ── Blow task: pick, place upwind, declare, and let the wind deliver ── + + +def _blow_place_sampler(state: State, subgoal_atoms: Set[GroundAtom], + rng: np.random.Generator, + objects: Sequence[Object]) -> Array: + """Put the block one slide-length upwind of the goal patch. + + The oracle's whole advantage in this env is this number. A learner + has to recover it from watching blocks slide; here it is read + straight off the ground-truth curve. + """ + del subgoal_atoms, objects + # pylint: disable-next=import-outside-toplevel + from predicators.ground_truth_models.domino.predicates import \ + _blow_slide_distance + regions = [o for o in state if o.type.name == "region"] + held = [ + o for o in state + if o.type.name == "domino" and state.get(o, "is_held") > 0.5 + ] + if not regions or len(held) != 1: + raise ValueError("blow place sampler: need a region and a held block") + region = regions[0] + x = float(state.get(region, "x")) - _blow_slide_distance() + y = float(state.get(region, "y")) + # A hair of jitter so backtracking can re-draw rather than retrying + # an identical pose, kept well inside the patch's own tolerance. + x += float(rng.uniform(-0.005, 0.005)) + z = float(state.get(held[0], "z")) + return np.array([x, y, z, 0.0], dtype=np.float32) + + +def _get_blow_processes( + types: Dict[str, Type], predicates: Dict[str, Predicate], + options: Dict[str, ParameterizedOption]) -> Set[CausalProcess]: + """Pick, place upwind, declare, and let the wind carry the block. + + Four processes and no grid. The one that matters is the last: the + wind is EXOGENOUS - the robot does not carry the block into the + goal, it arranges the world so that the wind will, and then says it + is done. That is the shape of the whole task, and it is why the + placement has to be right rather than merely somewhere. + """ + robot_type = types["robot"] + domino_type = types["domino"] + fan_type = types["fan"] + region_type = types["region"] + + HandEmpty = predicates["HandEmpty"] + Holding = predicates["Holding"] + FanOn = predicates["FanOn"] + FanOff = predicates["FanOff"] + ReadyToBlow = predicates["ReadyToBlow"] + InGoal = predicates["InGoal"] + + robot = Variable("?robot", robot_type) + block = Variable("?block", domino_type) + fan = Variable("?fan", fan_type) + region = Variable("?region", region_type) + + processes: Set[CausalProcess] = set() + + # Pick the block up. + processes.add( + EndogenousProcess( + "PickBlock", [robot, block], {LiftedAtom(HandEmpty, [robot])}, + set(), set(), {LiftedAtom(Holding, [robot, block])}, + {LiftedAtom(HandEmpty, [robot])}, + DiscreteGaussianDelay(mu=torch.tensor(3.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["Pick"], [robot, block], + _pick_sampler)) + + # Put it down one slide-length upwind of the patch. + processes.add( + EndogenousProcess( + "PlaceUpwind", [robot, block, region], + {LiftedAtom(Holding, [robot, block])}, set(), set(), { + LiftedAtom(HandEmpty, [robot]), + LiftedAtom(ReadyToBlow, [block, region]) + }, {LiftedAtom(Holding, [robot, block])}, + DiscreteGaussianDelay(mu=torch.tensor(3.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["Place"], [robot], + _blow_place_sampler)) + + # Say it is finished, and the fan starts. + processes.add( + EndogenousProcess( + "DeclareFinished", [robot, fan], {LiftedAtom(FanOff, [fan])}, + set(), set(), {LiftedAtom(FanOn, [fan])}, + {LiftedAtom(FanOff, [fan])}, + DiscreteGaussianDelay(mu=torch.tensor(1.0), + sigma=torch.tensor(0.1)), + torch.tensor(1.0), options["DeclareFinished"], [robot], + _declare_sampler)) + + # The gust. Exogenous: the robot never carries the block in. + processes.add( + ExogenousProcess( + "WindCarriesToGoal", [fan, block, region], { + LiftedAtom(FanOn, [fan]), + LiftedAtom(ReadyToBlow, [block, region]) + }, set(), set(), {LiftedAtom(InGoal, [block, region])}, + set(), + DiscreteGaussianDelay(mu=torch.tensor(float( + CFG.domino_blow_wind_steps)), + sigma=torch.tensor(2.0)), + torch.tensor(1.0))) + + return processes diff --git a/predicators/ground_truth_models/domino/types.py b/predicators/ground_truth_models/domino/types.py index a43c54c08..b5d70e803 100644 --- a/predicators/ground_truth_models/domino/types.py +++ b/predicators/ground_truth_models/domino/types.py @@ -20,7 +20,8 @@ def get_env_names(cls) -> Set[str]: return { "pybullet_domino", "pybullet_domino_real", "pybullet_domino_real_geometry", "pybullet_domino_fan", - "pybullet_domino_declare" + "pybullet_domino_declare", + "pybullet_domino_blow" } @classmethod diff --git a/predicators/settings.py b/predicators/settings.py index 27c1d4149..a3b6f33c3 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -906,10 +906,14 @@ class GlobalSettings: # because there the wind tips a block in about two steps and every # force above threshold looks the same (see # scripts/domino_debug/probe_wind_identifiability.py). - domino_blow_wind_force = 0.35 + domino_blow_wind_force = 2.5 # Steps the fan blows for once the robot has declared. Bounded so # the block travels a finite, repeatable distance rather than being - # pushed until the episode ends. + # pushed until the episode ends. Measured: at 2.5 N this gust slides + # the block 11.6 cm, and the response is steep and monotone (1.5 N -> + # 3.7 cm, 2.5 -> 11.6, 4.0 -> 30.9) which is exactly the gradient the + # cascade env's saturating topple does not provide. Past ~6 N the + # block is launched off the table rather than slid. domino_blow_wind_steps = 60 # Sides carrying a fan + switch in pybullet_domino_fan, in order # left, right, down, up. One is the point of the task: the robot has diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index 5169613c7..1e8773048 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -428,6 +428,47 @@ ENVS: # and the turn geometry was tuned for a robot push anyway. domino_test_turn_ratio: 0.0 domino_train_turn_ratio: 0.0 + # Place a block so the WIND carries it into a goal region. Not a + # cascade: one block, one gust, one patch. The robot picks the block + # up, puts it down upwind of the patch, and declares finished. + # + # This is the env where the wind's strength is actually learnable. + # In domino_fan the wind tips a standing block in ~2 steps, so every + # force above threshold looks identical and wind_force cannot be + # fitted. Here the wind pushes through the centre of mass, the block + # slides, and distance travelled is steep and monotone in the force + # (1.8 N -> 5.8 cm, 2.5 -> 11.5, 3.2 -> 19.3). Against a 4 cm goal + # half-width the robot must know the force to about a sixth. + domino_blow: + NAME: "pybullet_domino_blow" + SKIP: True # un-skipped by run_rung.sh --blow + FLAGS: + max_initial_demos: 0 + excluded_objects_in_state_str: "loc,rot,angle,direction" + horizon: 500 + domino_initialize_at_finished_state: False + domino_use_continuous_place: True + process_planning_heuristic_weight: 2.0 + domino_has_glued_dominos: False + keep_failed_demos: True + predicate_invent_invent_derived_predicates: True + pybullet_birrt_extend_num_interp: 20 + pybullet_birrt_path_subsample_ratio: 2 + fan_known_controls_relation: True + fan_fans_blow_opposite_direction: False + # Lay each chain along one fan's wind axis. Without this the + # generator points chains anywhere, and a chain across the wind + # cannot cascade however hard the fan blows - the task is + # unsolvable before a planner sees it. + # Sized so the block moves before Wait declares the scene settled + # (onset 7 steps at 1.5 N against Wait's ~11), not just to clear + # the 0.123 N tipping threshold. See settings.py. + domino_blow_wind_force: 2.5 + domino_blow_wind_steps: 60 + # Straight chains only. Alignment guarantees the FIRST leg runs + # downwind; a turn puts the rest of the chain across the wind, + # and the turn geometry was tuned for a robot push anyway. + domino_train_turn_ratio: 0.0 # coffee: # NAME: "pybullet_coffee" # FLAGS: diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index 16e488451..a1eec55b1 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -12,6 +12,11 @@ # # scripts/domino_fan/run_rung.sh --declare 1 # +# --blow is the place-it-so-the-wind-carries-it task, where the wind's +# strength IS fittable and rung 3 is therefore worth running: +# +# scripts/domino_fan/run_rung.sh --blow 1 +# # SEED=1 repeats a rung on another seed, for a second opinion: # # SEED=1 scripts/domino_fan/run_rung.sh 4 @@ -38,6 +43,9 @@ ENV_KEY="domino_fan" if [ "${1:-}" = "--declare" ]; then ENV_KEY="domino_declare" shift +elif [ "${1:-}" = "--blow" ]; then + ENV_KEY="domino_blow" + shift fi # Which random seed to run. A rung's result on one seed is an anecdote: From b2032b48abb5d273087d7169a255418e2fede92d Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 18:21:05 -0400 Subject: [PATCH 44/48] domino_blow: the wind must knock it FLAT, which is what makes the task a task Michi's design call, and it resolves the tension I was stuck on. The goal is now the block lying FLAT inside the region, not merely present there. A robot cannot place a domino on its side - Place sets blocks upright - so a block lying in the patch can only have been put there by the wind. Without that word the task had a trivial answer: pick the block up, put it down in the region, never touch the fan. Making it flat AND learnable took the right pair of settings, both measured: * the gust acts 0.4 of the way up the block, so it tips * it does NOT stop at the topple, so it keeps pushing the fallen block and the landing point still encodes the force Cut the wind at the topple and every force lands in the same place (8.78 cm at 1.3 N, 8.93 at 2.0) - the saturation that made domino_fan's wind unfittable. Let it keep pushing and the spread comes back: over a 30-step gust, 12.0 cm at 1.5 N, 14.4 at 2.0, 16.3 at 2.5, 20.3 at 3.0, 26.4 at 3.5, always flat. Two orientation bugs found by tracing the block through the plan rather than reading the code. The generator staged the block at yaw 0, presenting its narrow edge to a +x gust: it slid and never tipped at any force or lever I tried. And the place sampler then dropped it back to yaw 0 even after the generator was fixed - 5.0 cm and roll 0.000, where a turned block gives 14.4 cm and flat. Verified in-process end to end: Pick, Place at 0.5491 against a target of 0.5468, TurnFanOn, and the block ends flat at 0.6925 against a goal of 0.6841 - 0.83 cm, inside a 4 cm tolerance. The launcher's oracle run still scores 0/1, so something in the refinement path differs from the traced plan. Committing the mechanism while that is chased. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- .../components/goal_region_component.py | 20 +++- predicators/envs/pybullet_domino/env.py | 29 ++++-- .../task_generators/domino_task_generator.py | 28 ++++-- .../ground_truth_models/domino/options.py | 1 - .../ground_truth_models/domino/predicates.py | 39 ++++++-- .../ground_truth_models/domino/processes.py | 91 ++++++++++++++----- predicators/settings.py | 20 ++-- scripts/configs/predicatorv3/envs/all.yaml | 8 ++ 8 files changed, 178 insertions(+), 58 deletions(-) diff --git a/predicators/envs/pybullet_domino/components/goal_region_component.py b/predicators/envs/pybullet_domino/components/goal_region_component.py index 158ca343d..c7014f311 100644 --- a/predicators/envs/pybullet_domino/components/goal_region_component.py +++ b/predicators/envs/pybullet_domino/components/goal_region_component.py @@ -169,7 +169,14 @@ def set_region_xy(self, x: float, y: float) -> None: # -- predicate ---------------------------------------------------- def _InGoal_holds(self, state: State, objects: Sequence[Object]) -> bool: - """The domino's centre lies inside the patch. + """The domino lies FLAT inside the patch. + + Flat, not merely present, and that word is what makes the task + a task. A robot cannot place a domino on its side - Place sets + blocks upright - so a block lying in the patch can only have + been put there by the wind. Without it the goal has a trivial + answer: pick the block up, put it down in the region, done, and + the fan is never needed at all. Centre rather than footprint overlap: a block half in and half out is a coin toss on the exact contact solve, and the reward @@ -178,6 +185,17 @@ def _InGoal_holds(self, state: State, objects: Sequence[Object]) -> bool: if len(objects) != 2: return False domino, region = objects + if state.get(domino, "is_held") > 0.5: + return False + # Roll is meaningful modulo pi: a box turned 180 degrees about + # its own width axis is the same box. + # pylint: disable-next=import-outside-toplevel + from predicators.envs.pybullet_domino.components.domino_component \ + import DominoComponent + roll = float(state.get(domino, "roll")) + roll = (roll + np.pi / 2) % np.pi - np.pi / 2 + if abs(roll) < DominoComponent.domino_roll_threshold: + return False dx = abs(float(state.get(domino, "x")) - float(state.get(region, "x"))) dy = abs(float(state.get(domino, "y")) - float(state.get(region, "y"))) return (dx <= float(state.get(region, "half_x")) diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index b56e20dd7..cead1be4f 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -1153,13 +1153,13 @@ def _domain_specific_step(self) -> None: super()._domain_specific_step() -class PyBulletDominoBlowEnv(PyBulletDominoDeclareEnv): +class PyBulletDominoBlowEnv(PyBulletDominoFanEnv): """Place a block so the wind carries it INTO a goal region. The scene reads left to right: fan, goal patch, staged block. The robot picks the block up, puts it down somewhere between the fan - and the patch, and declares finished; the fan then blows for a - bounded number of steps and the block slides. It scores if the + and the patch, and presses the fan's switch; the fan then blows for + a bounded number of steps and the block slides. It scores if the block comes to rest inside the patch. This task exists because of what pybullet_domino_fan could NOT @@ -1211,12 +1211,21 @@ def _set_domain_specific_state(self, state: State) -> None: def _wire_blow_target(self, state: State) -> None: """Aim the fan at the block it is supposed to move. - z_offset 0.0 is the whole difference from the cascade envs: a - force through the centre of mass is pure translation. In - pybullet_domino_fan that same setting was a BUG - the start - block slid a metre and a half into the target without ever - tipping, "solving" a bridge task with no bridge - and here it - is the mechanism. + Two settings, and the task depends on both. + + The lever (0.4 of the block's height, as in the cascade env) is + what makes the block END UP FLAT, and flat is what stops the + task having a trivial answer: the robot cannot place a domino + on its side, so a block lying in the goal can only have been + put there by the wind. + + stop_when_toppled=False is what keeps the distance LEARNABLE. + Cut the wind at the topple and the block always lands about the + same place - 8.78 cm at 1.3 N and 8.93 at 2.0 - which is the + saturation that made pybullet_domino_fan's wind unfittable. + Let the gust keep pushing the fallen block and the landing point + spreads out again: 11.8 cm at 1.2 N, 23.4 at 2.6, smooth and + monotone in between. """ if self._fan_component is None or self._domino_component is None: return @@ -1233,7 +1242,7 @@ def _wire_blow_target(self, state: State) -> None: state.get(block, "y"))) self._fan_component.set_wind_target( block.id, - z_offset=0.0, + z_offset=0.4 * self._domino_component.domino_height, stop_when_toppled=False, force=CFG.domino_blow_wind_force) diff --git a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py index 82a88fa70..4687366dc 100644 --- a/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py +++ b/predicators/envs/pybullet_domino/task_generators/domino_task_generator.py @@ -366,13 +366,14 @@ def _generate_blow_task( return None obj_dict: Dict[Object, Dict[str, Any]] = {} - # rotation 0 makes the block face along +y; the wind is axial and - # pushes through the centre of mass, so facing does not matter - # here the way it does for a topple. + # pi/2 turns the block's WIDE face into the wind. With rotation + # 0 it presents its narrow edge, which a +x gust shoves happily + # and can barely tip - measured: nothing toppled at any force or + # lever until the block was turned to face the wind. obj_dict[dominos[0]] = self.domino.place_domino(0, stage_x, lane_y, - 0.0, + np.pi / 2, rng=rng, task_idx=task_idx) # Every other domino body this env owns is parked out of view: @@ -391,6 +392,14 @@ def _generate_blow_task( self.robot: self.robot_init_state.copy() } init_dict.update(obj_dict) + # Aim the fan down the lane BEFORE its init entries are read. + # Reading them first records the fan at its rail default while + # the body ends up on the lane, and the state then disagrees + # with the world by a third of a metre - the same misalignment + # the cascade generator fixes for the same reason. + for component in self.additional_components: + if hasattr(component, "set_lateral_alignment"): + component.set_lateral_alignment(lane_y) for component in self.additional_components: if hasattr(component, "get_init_dict_entries"): init_dict.update(component.get_init_dict_entries(rng)) @@ -401,11 +410,12 @@ def _generate_blow_task( [dominos[0], self._goal_region.region]) } goal_nl = ( - "Pick up the block and put it down so that when you declare " - "you have finished, it ends up inside the green goal region. " - "The block starts downwind of the region, so leaving it where " - "it is cannot work. Declaring finished is the only thing that " - "starts the fan, and the fan blows for a limited time.") + "Pick up the block and put it down so that when the fan is " + "switched on, the wind knocks it over and it ends up lying " + "FLAT inside the green goal region. Putting the block down " + "in the region is not enough - you cannot place it on its " + "side, so only the wind can leave it flat. The fan blows for " + "a limited time once it is switched on.") return EnvironmentTask(init_state, goal_atoms, goal_nl=goal_nl) @property diff --git a/predicators/ground_truth_models/domino/options.py b/predicators/ground_truth_models/domino/options.py index 9f5c7887f..21deacfed 100644 --- a/predicators/ground_truth_models/domino/options.py +++ b/predicators/ground_truth_models/domino/options.py @@ -52,7 +52,6 @@ def _skill_robot_env_cls(env_name: str) -> TypingType[PyBulletEnv]: # for the same reason as _WIND_STARTED_ENVS below. _DECLARE_TRIGGER_ENVS = frozenset({ "pybullet_domino_declare", - "pybullet_domino_blow", }) _WIND_STARTED_ENVS = frozenset({ diff --git a/predicators/ground_truth_models/domino/predicates.py b/predicators/ground_truth_models/domino/predicates.py index 9cb6ca473..cff93a0ab 100644 --- a/predicators/ground_truth_models/domino/predicates.py +++ b/predicators/ground_truth_models/domino/predicates.py @@ -57,12 +57,12 @@ def _blow_slide_distance() -> float: an agent approach cannot read it off the state. Knowing it is the whole content of the task. """ - # A quadratic least-squares fit to the measured curve, accurate to - # under a millimetre from 1.8 to 3.2 N (5.75 / 8.79 / 11.53 / 14.61 - # / 19.33 cm). Cheaper and clearer than shipping a table, and it + # A quadratic least-squares fit to the measured curve over the + # 30-step gust, from 1.5 to 3.5 N (12.02 / 14.35 / 16.33 / 20.33 / + # 26.36 cm). Cheaper and clearer than shipping a table, and it # extrapolates sensibly for a task generator that varies the force. force = CFG.domino_blow_wind_force - return max(0.0, 0.02081 * force * force - 0.00703 * force + 0.00273) + return max(0.0, 0.02691 * force * force - 0.06525 * force + 0.16024) def _blow_helper_predicates(types: Dict[str, Type]) -> Set[Predicate]: @@ -77,15 +77,36 @@ def _blow_helper_predicates(types: Dict[str, Type]) -> Set[Predicate]: region_type = types["region"] def _ready_holds(state: State, objects: Sequence[Object]) -> bool: + """The block is somewhere the gust can still deliver it. + + A CORRIDOR, from one slide-length upwind of the patch through to + the patch's far edge, rather than the single point the placement + aims at. Written as a point it was true only at the instant of + release: the wind then moved the block, the atom flipped, and + the Wait terminated after three steps with "atom change during + Wait" - killing the gust a twentieth of the way through its own + flight. It has to stay true while the thing it describes is + happening. + + Loosening it costs nothing, because it is not what makes the + task hard. The goal demands the block end up FLAT in the patch, + and no placement anywhere in this corridor achieves that on its + own. + """ domino, region = objects if state.get(domino, "is_held") > 0.5: return False - # The fan blows +x, so the block must start upwind by one slide. - want_x = float(state.get(region, "x")) - _blow_slide_distance() - dx = abs(float(state.get(domino, "x")) - want_x) + gx = float(state.get(region, "x")) + half_x = float(state.get(region, "half_x")) + half_y = float(state.get(region, "half_y")) + # The fan blows +x, so the block travels from upwind toward the + # patch. Half a patch of slack at the upwind end is the + # placement tolerance; the far edge closes the corridor. + lo = gx - _blow_slide_distance() - half_x + hi = gx + half_x + x = float(state.get(domino, "x")) dy = abs(float(state.get(domino, "y")) - float(state.get(region, "y"))) - return (dx <= float(state.get(region, "half_x")) - and dy <= float(state.get(region, "half_y"))) + return lo <= x <= hi and dy <= half_y return { Predicate("ReadyToBlow", [domino_type, region_type], _ready_holds) diff --git a/predicators/ground_truth_models/domino/processes.py b/predicators/ground_truth_models/domino/processes.py index da567cfca..a5bdecb1c 100644 --- a/predicators/ground_truth_models/domino/processes.py +++ b/predicators/ground_truth_models/domino/processes.py @@ -483,8 +483,7 @@ def _get_fan_processes( # what the process is grounded on. robot = Variable("?robot", robot_type) fan = Variable("?fan", fan_type) - if CFG.env in ("pybullet_domino_declare", - "pybullet_domino_blow"): + if CFG.env == "pybullet_domino_declare": # The robot announces it has finished building and the fan # starts. The option takes only the robot -- there is # nothing to reach for -- so the fan appears in the @@ -838,8 +837,18 @@ def _blow_place_sampler(state: State, subgoal_atoms: Set[GroundAtom], # A hair of jitter so backtracking can re-draw rather than retrying # an identical pose, kept well inside the patch's own tolerance. x += float(rng.uniform(-0.005, 0.005)) - z = float(state.get(held[0], "z")) - return np.array([x, y, z, 0.0], dtype=np.float32) + # The canonical release height, NOT the held block's current z: the + # Place option's release_z is where the GRIPPER opens (its declared + # range is 0.5-0.6), and the block's carried z is neither that nor + # inside it, so every refinement was asking for a drop the skill + # could not make. + # yaw pi/2 puts the block's WIDE face into the wind. Dropping it at + # yaw 0 leaves the narrow edge facing the gust, which the wind + # creeps along without ever tipping: measured 5.0 cm and roll 0.000 + # where the same force on a turned block gives 14.4 cm and flat. + # The generator stages the block turned; the placement has to keep + # it that way. + return np.array([x, y, _DOMINO_DROP_Z, np.pi / 2], dtype=np.float32) def _get_blow_processes( @@ -872,16 +881,24 @@ def _get_blow_processes( processes: Set[CausalProcess] = set() + # Predicates a pick or a place disturbs incidentally. Lifting a + # block off the table changes whether it is Upright and whether it + # is where the wind would take it; a process that does not declare + # those as ignorable is rejected in refinement for effects it never + # claimed, which is what stalled every skeleton at step 0. + Upright = predicates["Upright"] + incidental = {Upright, ReadyToBlow, InGoal} + # Pick the block up. processes.add( EndogenousProcess( "PickBlock", [robot, block], {LiftedAtom(HandEmpty, [robot])}, set(), set(), {LiftedAtom(Holding, [robot, block])}, {LiftedAtom(HandEmpty, [robot])}, - DiscreteGaussianDelay(mu=torch.tensor(3.0), + DiscreteGaussianDelay(mu=torch.tensor(4.0), sigma=torch.tensor(0.1)), torch.tensor(1.0), options["Pick"], [robot, block], - _pick_sampler)) + _pick_sampler, incidental)) # Put it down one slide-length upwind of the patch. processes.add( @@ -894,30 +911,62 @@ def _get_blow_processes( DiscreteGaussianDelay(mu=torch.tensor(3.0), sigma=torch.tensor(0.1)), torch.tensor(1.0), options["Place"], [robot], - _blow_place_sampler)) - - # Say it is finished, and the fan starts. + _blow_place_sampler, incidental)) + + # Press the switch, and the fan starts. HandEmpty is a precondition + # and not decoration: without it the planner is free to press while + # still holding the block, and its first skeleton did exactly that - + # PickBlock, , PlaceUpwind - which blows the gust across an + # empty table while the arm is still carrying the thing it was + # supposed to move. processes.add( EndogenousProcess( - "DeclareFinished", [robot, fan], {LiftedAtom(FanOff, [fan])}, - set(), set(), {LiftedAtom(FanOn, [fan])}, + "TurnFanOn", [robot, fan, block, region], { + LiftedAtom(FanOff, [fan]), + LiftedAtom(HandEmpty, [robot]), + # The switch is only worth pressing once the block is + # where the gust can deliver it. HandEmpty alone is true + # at t=0, so without this the planner's first skeleton + # pressed the switch before it had even picked the block + # up and blew the gust across an empty table. + LiftedAtom(ReadyToBlow, [block, region]) + }, set(), set(), {LiftedAtom(FanOn, [fan])}, {LiftedAtom(FanOff, [fan])}, DiscreteGaussianDelay(mu=torch.tensor(1.0), sigma=torch.tensor(0.1)), - torch.tensor(1.0), options["DeclareFinished"], [robot], - _declare_sampler)) + torch.tensor(1.0), options["TurnFanOn"], [robot, fan], + _switch_push_sampler)) # The gust. Exogenous: the robot never carries the block in. + # condition_overall as well as condition_at_start: the gust only + # delivers the block if the fan STAYS on and the block STAYS where + # it was put for the whole flight, which is what the cascade's own + # exogenous processes assert too. + wind_conditions = { + LiftedAtom(FanOn, [fan]), + LiftedAtom(ReadyToBlow, [block, region]) + } processes.add( ExogenousProcess( - "WindCarriesToGoal", [fan, block, region], { - LiftedAtom(FanOn, [fan]), - LiftedAtom(ReadyToBlow, [block, region]) - }, set(), set(), {LiftedAtom(InGoal, [block, region])}, - set(), - DiscreteGaussianDelay(mu=torch.tensor(float( - CFG.domino_blow_wind_steps)), - sigma=torch.tensor(2.0)), + "WindCarriesToGoal", [fan, block, region], wind_conditions, + wind_conditions.copy(), set(), + {LiftedAtom(InGoal, [block, region])}, set(), + # Delay is in PROCESS steps, not simulator steps. Handing it + # the gust's 60 simulator steps put the effect beyond the + # planner's lookahead and every skeleton was exhausted + # without the goal ever becoming true. The cascade's own + # exogenous processes use 1-4 for the same reason. + DiscreteGaussianDelay(mu=torch.tensor(12.0), + sigma=torch.tensor(0.5)), torch.tensor(1.0))) + # Wait. No preconditions, no effects: it exists so the planner can + # let TIME pass. The gust needs about sixty simulator steps to carry + # the block, and without a Wait in the skeleton the episode ends the + # instant the robot finishes speaking. + processes.add( + EndogenousProcess("Wait", [robot], set(), set(), set(), set(), set(), + ConstantDelay(1), torch.tensor(1.0), + options["Wait"], [robot], null_sampler)) + return processes diff --git a/predicators/settings.py b/predicators/settings.py index a3b6f33c3..9ddcec614 100644 --- a/predicators/settings.py +++ b/predicators/settings.py @@ -898,15 +898,17 @@ class GlobalSettings: # longer holds. domino_fan_wind_force = 1.5 - # Wind force for the blow-to-goal task (N). Applied at the block's - # CENTRE OF MASS, so the block slides instead of tipping - the whole - # point of that task is that the distance travelled is a continuous, - # monotone function of this number, which is what makes it fittable - # at all. In pybullet_domino_fan the same parameter is NOT fittable, + # Wind force for the blow-to-goal task (N). Applied 0.4 of the way + # up the block, so the gust tips it AND keeps pushing it once it is + # down: the block ends up flat (which the robot cannot achieve by + # placing, so the goal cannot be reached without the wind) while the + # distance travelled stays a continuous, monotone function of this + # number - over a 30-step gust: 12.0 cm at 1.5 N, 14.4 at 2.0, + # 16.3 at 2.5, 20.3 at 3.0, 26.4 at 3.5. In pybullet_domino_fan the same parameter is NOT fittable, # because there the wind tips a block in about two steps and every # force above threshold looks the same (see # scripts/domino_debug/probe_wind_identifiability.py). - domino_blow_wind_force = 2.5 + domino_blow_wind_force = 2.0 # Steps the fan blows for once the robot has declared. Bounded so # the block travels a finite, repeatable distance rather than being # pushed until the episode ends. Measured: at 2.5 N this gust slides @@ -914,7 +916,11 @@ class GlobalSettings: # 3.7 cm, 2.5 -> 11.6, 4.0 -> 30.9) which is exactly the gradient the # cascade env's saturating topple does not provide. Past ~6 N the # block is launched off the table rather than slid. - domino_blow_wind_steps = 60 + # Thirty, not sixty, because the plan gets exactly ONE Wait however + # long the wind process's delay is made, and a Wait ends when the + # scene's atoms change. A 60-step gust was still pushing when its + # Wait expired at 40 steps and the block stopped short. + domino_blow_wind_steps = 30 # Sides carrying a fan + switch in pybullet_domino_fan, in order # left, right, down, up. One is the point of the task: the robot has # a single switch to find and press. Four is the ball task's layout diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index 1e8773048..d78205b10 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -463,6 +463,14 @@ ENVS: # Sized so the block moves before Wait declares the scene settled # (onset 7 steps at 1.5 N against Wait's ~11), not just to clear # the 0.123 N tipping threshold. See settings.py. + # Exactly one block. Spare bodies are not harmless scenery here: + # they stay MovableBlock wherever they are parked, so the planner + # grounds PickBlock over them and builds skeletons that reach for + # a block ten metres off the table, which then cannot be refined. + domino_train_num_dominos: [1] + domino_test_num_dominos: [1] + domino_train_num_targets: [0] + domino_test_num_targets: [0] domino_blow_wind_force: 2.5 domino_blow_wind_steps: 60 # Straight chains only. Alignment guarantees the FIRST leg runs From 135fb6869b8d5aef44771812eb04c80aa4274459 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Tue, 1 Sep 2026 18:36:38 -0400 Subject: [PATCH 45/48] domino_blow: oracle solves it, 1/1 Two bugs, and the second is the one that mattered. The gust budget was refilled by _set_domain_specific_state, which the executor calls far more often than once per episode - it reconstructs state during execution. The counter never reached zero, the fan never switched off, and the block was pushed past the goal. It now refills only when the incoming state has the fan off, which is to say at the start of an episode. And envs/all.yaml pinned domino_blow_wind_steps: 60 while settings.py had been tuned to 30. Force and gust length are calibrated together against the measured slide curve, so pinning one of them decoupled the pair: the launcher blew for twice as long as the oracle's slide model assumed and the block sailed past every time. The override is gone; settings.py governs both. Worth recording how this was found, because reading the code did not find it. Driving the plan by hand succeeded (0.06 cm error) while the launcher scored zero on the same plan, and the difference only became visible after logging where the gust actually left the block. The log said budget=60 where the model assumed 30, and that was the whole mystery. Result: block lands 1.3 cm from the goal centre, flat, inside the region. Tasks solved 1/1, reward 1.000. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- predicators/envs/pybullet_domino/env.py | 46 +++++++++++++++++-- .../ground_truth_models/domino/processes.py | 9 +++- scripts/configs/predicatorv3/envs/all.yaml | 7 ++- 3 files changed, 55 insertions(+), 7 deletions(-) diff --git a/predicators/envs/pybullet_domino/env.py b/predicators/envs/pybullet_domino/env.py index cead1be4f..f1a901b87 100644 --- a/predicators/envs/pybullet_domino/env.py +++ b/predicators/envs/pybullet_domino/env.py @@ -1203,9 +1203,18 @@ def get_name(cls) -> str: def _set_domain_specific_state(self, state: State) -> None: super()._set_domain_specific_state(state) - # A fresh episode gets a fresh gust budget, and the wind aims at - # the movable block through its centre so the block slides. - self._wind_steps_left = CFG.domino_blow_wind_steps + # A fresh gust budget, but ONLY when the incoming state has the + # fan off - which is to say, at the start of an episode. + # + # _set_state is called far more often than once per episode: the + # executor reconstructs state during execution, and refilling + # the budget on every one of those meant the counter never + # reached zero, the fan never switched off, and the block was + # pushed straight past the goal. The trace that drove the plan + # by hand never hit it, which is exactly why it passed while the + # launcher scored zero. + if self._fan_component is None or not self._fan_component.any_fan_on(): + self._wind_steps_left = CFG.domino_blow_wind_steps self._wire_blow_target(state) def _wire_blow_target(self, state: State) -> None: @@ -1256,9 +1265,40 @@ def _domain_specific_step(self) -> None: if self._fan_component is None: return if self._fan_component.any_fan_on() and self._wind_steps_left > 0: + if self._wind_steps_left == CFG.domino_blow_wind_steps: + logging.info("[blow] gust starts, budget=%d", + self._wind_steps_left) self._wind_steps_left -= 1 if self._wind_steps_left == 0: self._fan_component.set_fans_on(False) + self._log_gust_outcome() + + + def _log_gust_outcome(self) -> None: + """Where the gust left the block, once per episode. + + The task turns entirely on this one number, and reading it off + a video is not reading it. Logged at the moment the gust ends, + which is the moment the answer is decided. + """ + try: + state = self._get_state() + except Exception: # pylint: disable=broad-except + return + blocks = [o for o in state if o.type.name == "domino"] + regions = [o for o in state if o.type.name == "region"] + if not blocks or not regions: + return + block, region = blocks[0], regions[0] + roll = float(state.get(block, "roll")) + roll = (roll + np.pi / 2) % np.pi - np.pi / 2 + gx = float(state.get(region, "x")) + half = float(state.get(region, "half_x")) + bx = float(state.get(block, "x")) + logging.info( + "[blow] gust over: block x=%.4f roll=%.3f | goal x=%.4f " + "+/- %.3f | dx=%.4f | flat=%s in_x=%s", bx, roll, gx, half, + bx - gx, abs(roll) >= 0.087, abs(bx - gx) <= half) class PyBulletDominoFanRampEnv(PyBulletDominoComposedEnv): diff --git a/predicators/ground_truth_models/domino/processes.py b/predicators/ground_truth_models/domino/processes.py index a5bdecb1c..0750348cb 100644 --- a/predicators/ground_truth_models/domino/processes.py +++ b/predicators/ground_truth_models/domino/processes.py @@ -935,7 +935,7 @@ def _get_blow_processes( DiscreteGaussianDelay(mu=torch.tensor(1.0), sigma=torch.tensor(0.1)), torch.tensor(1.0), options["TurnFanOn"], [robot, fan], - _switch_push_sampler)) + _switch_push_sampler, incidental)) # The gust. Exogenous: the robot never carries the block in. # condition_overall as well as condition_at_start: the gust only @@ -964,9 +964,14 @@ def _get_blow_processes( # let TIME pass. The gust needs about sixty simulator steps to carry # the block, and without a Wait in the skeleton the episode ends the # instant the robot finishes speaking. + # The Wait's ignore_effects are the point of the Wait. Everything + # this task is about happens DURING it - the block tips, slides and + # arrives - and a Wait that does not declare those changes ignorable + # is cut short by its own executor with "atom change during Wait". processes.add( EndogenousProcess("Wait", [robot], set(), set(), set(), set(), set(), ConstantDelay(1), torch.tensor(1.0), - options["Wait"], [robot], null_sampler)) + options["Wait"], [robot], null_sampler, + incidental)) return processes diff --git a/scripts/configs/predicatorv3/envs/all.yaml b/scripts/configs/predicatorv3/envs/all.yaml index d78205b10..f6ca83ba2 100644 --- a/scripts/configs/predicatorv3/envs/all.yaml +++ b/scripts/configs/predicatorv3/envs/all.yaml @@ -471,8 +471,11 @@ ENVS: domino_test_num_dominos: [1] domino_train_num_targets: [0] domino_test_num_targets: [0] - domino_blow_wind_force: 2.5 - domino_blow_wind_steps: 60 + # Left to settings.py: force and gust length are calibrated + # together against the measured slide curve, and pinning one of + # them here silently decoupled the pair - the launcher blew for + # 60 steps while the oracle's slide model assumed 30, so the + # block sailed past the goal every time. # Straight chains only. Alignment guarantees the FIRST leg runs # downwind; a turn puts the rest of the chain across the wind, # and the turn geometry was tuned for a robot push anyway. From 8488dbdd0567284de0f5f5b7df9222bf240f5095 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 2 Sep 2026 11:04:44 -0400 Subject: [PATCH 46/48] domino_blow: badge it on the dashboard, and stop warning about a scene that is correct The dashboard had no trigger badge for this env, so its rows read as unclassified beside the two fan ladders. BLOW now sits beside BUTTON and DECLARE, and its tooltip says the thing that distinguishes it: the only one of the three where the wind's strength can be fitted. The parent fan env warned 'no start (green) domino in this task' on every reset of a blow task. There is no green start block here by design - one plain block, no chain - and PyBulletDominoBlowEnv re-aims the wind immediately afterwards, so the line was noise that read like a broken scene. Rung 2 was stopped at 1h15m rather than finished. It had converged: 26 of 41 gust attempts landed the block flat inside the goal, the last several clustered at dx = -0.02 m against a 0.04 m tolerance, so it had found the placement band and was validating rather than searching. No score was captured. 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not a fault: the blow task has one plain block + # and no chain, so there is no green start block to aim at + # and PyBulletDominoBlowEnv re-aims the wind itself right + # after this. Warning about it filled the logs with a line + # that reads like a broken scene. + return logging.warning( "Fan env has no start (green) domino in this task; leaving the " "wind target unchanged.") @@ -1265,9 +1272,6 @@ def _domain_specific_step(self) -> None: if self._fan_component is None: return if self._fan_component.any_fan_on() and self._wind_steps_left > 0: - if self._wind_steps_left == CFG.domino_blow_wind_steps: - logging.info("[blow] gust starts, budget=%d", - self._wind_steps_left) self._wind_steps_left -= 1 if self._wind_steps_left == 0: self._fan_component.set_fans_on(False) diff --git a/scripts/log_viewer.py b/scripts/log_viewer.py index d93e9ef3a..754a890ee 100644 --- a/scripts/log_viewer.py +++ b/scripts/log_viewer.py @@ -1816,6 +1816,8 @@ def ansi_to_html(text: str) -> str: border: 1px solid var(--border2); color: var(--muted); } .trig-declare { border-color: var(--ok); color: var(--ok); background: var(--ok-dim); } +.trig-blow { border-color: var(--warn); color: var(--warn); + background: var(--warn-dim); } /* The "what is happening right now" panel above the run table. */ .livepanel { border: 1px solid var(--border); border-radius: 8px; background: var(--panel); padding: 12px 14px; margin: 0 0 14px; } @@ -2450,6 +2452,9 @@ def status_chip(r: Dict[str, Any], summary: Dict[str, Any], live: LiveProcs, "domino_fan": ("BUTTON", "the robot presses a switch to start the fan"), "domino_declare": ("DECLARE", "no switch: the robot declares finished and the fan starts"), + "domino_blow": + ("BLOW", "place a block so the wind knocks it FLAT into a goal region; " + "the only env here where the wind's strength is fittable"), } @@ -2591,7 +2596,11 @@ def rung_badge(exp: str) -> str: return "" num, what = entry word, how = trigger - cls = "trig trig-declare" if env_key == "domino_declare" else "trig" + cls = "trig" + if env_key == "domino_declare": + cls = "trig trig-declare" + elif env_key == "domino_blow": + cls = "trig trig-blow" return (f"RUNG {num}" f"{word}") From 47d223715556a53ea288c16fc578a9a6dd14d796 Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Wed, 2 Sep 2026 13:40:26 -0400 Subject: [PATCH 47/48] domino_blow: give rung 4's fit room, because here the fit matters The rung 4 budget caps were set for domino_fan, where the parameter fit provably could not move (delta 0.0000 on every param, five times) and was costing five hours per cycle. On domino_blow the same fit is load-bearing: the wind's magnitude decides how far the block travels, and a block that travels wrong misses the goal region. A fit starved of sweep passes would fail the task for a reason that is not the agent's. Two passes instead of one, on this env only. A wide posterior is fine here - the rung has to do structural learning AND parameter learning, and it is the pair that matters rather than the precision. Claude-Session: https://claude.ai/code/session_01XWzrba9c7sQ45YV5KCtvMm --- scripts/domino_fan/run_rung.sh | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/scripts/domino_fan/run_rung.sh b/scripts/domino_fan/run_rung.sh index a1eec55b1..ca1c93fff 100755 --- a/scripts/domino_fan/run_rung.sh +++ b/scripts/domino_fan/run_rung.sh @@ -136,8 +136,15 @@ for a in arms: flags += [ " num_online_learning_cycles: 2", " code_sim_learning_rollout_grid_seed_points: 5", - " code_sim_learning_rollout_grid_sweep_passes: 1", ] + # One sweep pass is right where the fit cannot succeed anyway. + # On domino_blow it can and it must: the wind's magnitude sets + # how far the block travels, and a block that travels wrong + # misses the goal region, so a fit starved of passes fails the + # task for a reason that is not the agent's. A wide posterior is + # acceptable there; a fit that never moves is not. + flags.append(" code_sim_learning_rollout_grid_sweep_passes: " + + ("2" if env_key == "domino_blow" else "1")) lines += [" FLAGS:"] + flags print("\n".join(lines)) PYEOF From 5d701d7130cf30a63def169f5a1a89033d7e3ede Mon Sep 17 00:00:00 2001 From: Michelangelo Naim Date: Thu, 3 Sep 2026 16:45:21 -0400 Subject: [PATCH 48/48] domino_blow: keep the oracle gif and the simulator the agent wrote The gif is the oracle's 1/1 solve: pick, place upwind, press the switch, and the wind knocks the block flat into the goal region. The simulator is from the first rung-4 attempt on this env. It decomposed the wind into force and torque with the correct cross-product form, and added a creep term for the fallen block sliding on - the behaviour that makes landing distance encode the wind's strength. Four self-named parameters, and a fit that moved wind_force by 20-70% between refits. --- docs/envs/domino_blow/discovered_simulator.py | 179 ++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 docs/envs/domino_blow/discovered_simulator.py diff --git a/docs/envs/domino_blow/discovered_simulator.py b/docs/envs/domino_blow/discovered_simulator.py new file mode 100644 index 000000000..20c67410e --- /dev/null +++ b/docs/envs/domino_blow/discovered_simulator.py @@ -0,0 +1,179 @@ +# ============================================================================= +# DECISION RECORD - pybullet_domino_blow residual simulator +# ============================================================================= +# WHAT THE BASE SIM ALREADY CARRIES (do NOT re-model): +# * Robot motion / IK / grasping; Pick and Place reproduce the recorded +# action counts and the placed pose exactly (probe: Pick=51 actions, +# Place=27 actions, placed x within 0.0003 of the recording). +# * The switch -> fan.is_on toggle (TurnFanOn flips both switch_0.is_on and +# fan_0.is_on, 24 actions into its 41-action rollout). +# * All rigid-body physics of the domino: the gravity topple in traj 1 +# (the Pick at grasp_z_offset=0.08 knocked the block over) is reproduced +# by the base sim on replay. +# +# WHAT IS MISSING FROM THE BASE SIM = WIND (the whole residual): +# With fan_0.is_on == 1 the base sim leaves the domino perfectly static +# (journal iter0: 8 consecutive Waits, zero motion at 4 different x). +# In the recorded data the fan visibly drives the block. This is an +# exogenous influence the engine knows nothing about => diagnostic-ladder +# case 2 => modelled with `cmds` (physics commands), NOT feature writes. +# +# PHYSICAL_PARAMS: DELIBERATELY NOT DECLARED. +# Open-loop rollout sweep of all five registry parameters (lateral_friction, +# mass, restitution, rolling_friction, spinning_friction), each alone across +# its full box, was FLAT: SSE 62.2-65.3 against a 62.62 baseline (<5% spread, +# far under the 3x consistency bar). The open-loop divergence is entirely +# the missing wind, not mis-set rigid-body physics. Declaring any of them +# would fit noise. +# +# TWO WIND REGIMES (both required; a single channel cannot produce both): +# (A) STANDING / TIPPING block -> force + toppling moment. +# Evidence: traj 0, fan on at step 102, the block goes +# roll 0 -> pi/2 in 6 actions with dx per action rising then falling +# (0.006, 0.013, 0.028, 0.041, 0.019, 0.008) - the signature of a +# rotation about the leading bottom edge, i.e. engine physics driven +# by an external push. A pure COM force reproduces the topple only in +# a knife-edge band (F=0.14 N: no topple at all; F=0.15 N: topple), so +# the wind is modelled as force + an explicit moment (equivalent to a +# force applied above the COM, which is what wind on a tall face is). +# Torque axis = z_hat x wind_dir, so it tips the block DOWNWIND. +# (B) FLAT (already fallen) block -> constant-velocity creep. +# Evidence: in BOTH trajectories a fallen block drifts downwind at a +# PERFECTLY constant 0.001215 m/action (5 significant figures, 22 +# consecutive steps in traj 1, 14 in traj 0, at two different x). +# Constant velocity is incompatible with a Coulomb-friction + constant +# force model (measured in-probe: F=0.2 N -> the flat block does not +# move at all; F=0.45 N -> it runs away accelerating at 0.108 m/action; +# there is no stable creep regime in between). So regime B is a +# kinematic velocity override. It does NOT fight the engine: with the +# small wind force the base sim leaves a flat block motionless. +# +# LATENT (partial observability): +# `blow_steps` per fan - actions elapsed since is_on went high. The task +# statement says the fan blows for a LIMITED time, but no expiry is +# observable in the data (wind still driving the block 22 actions after +# switch-on in traj 1, 20 in traj 0). The counter is therefore carried but +# its cutoff is set beyond anything the data can see; it is NOT a ParamSpec +# because there is no signal to fit it with. Later cycles that observe an +# expiry should turn _BLOW_DURATION_STEPS into a fitted parameter. +# +# RESIDUAL_FEATURES = domino x, z, roll: the pose dimensions the wind moves. +# They are scored against observations but NOT overwritten at test time - +# the engine moves them, which is exactly what the command channel wants. +# +# VALIDATION ANCHOR (real data, used to size placements): +# recorded Place(x=0.570) -> block lands at 0.57275 -> after the full +# TurnFanOn rollout (17 wind actions) it is FLAT at x = 0.7012. +# this model, same plan: x = 0.7042. Topple+creep offset ~= +0.1285. +# +# FIT / VALIDATION (this cycle): +# sim.fit -> joint rollout system-ID, 3 of 4 motion segments explainable +# (the dropped one is traj 1's Pick knocking the block over - +# prolonged robot-object contact, not repeatable under replay). +# rollout SSE 21.14 -> 0.0304 (99.9% reduction). +# sim.residuals(rollout=True) at these inits: wind segments RMS 0.0138 and +# 0.00064, against a no-rule baseline of 0.0883 and 0.0223. +# sim.refine(require_goal=True) on +# Pick[0.06]; Place[0.5287,1.3728,0.55,1.5708]; TurnFanOn[0.1,0.11] +# -> SUCCESS, goal atoms held. +# continuous sim.run of the same plan -> Goal reached: True, block flat at +# x = 0.6575 (region centre 0.6572); 5/5 trials reached the goal. +# Placement margin: every x in [0.511, 0.547] (the whole AtLaunchPose band) +# lands the block inside the region. The one real-data +# observation of a FAILING placement sits at |along| = 0.044, +# 2.4x the learned launch_tol of 0.018 - a comfortable margin, +# not a knife edge. +# +# NOTE for later cycles: no Wait steps in the plan. In this session's probe a +# Wait runs to quiescence (>= 40 actions, and NEVER quiesces while the wind +# creeps the block, hitting the 1000-action option cap), whereas every Wait +# in the recorded real trajectories consumed exactly 1 action. Because that +# discrepancy is unresolved, the plan is built so no Wait is needed: the +# topple and the whole useful slide happen inside the TurnFanOn rollout +# itself (the switch flips 24 actions into its 41-action rollout, leaving 17 +# actions of wind). +# ============================================================================= +import numpy as np + +# Half-width of the "lying flat" band around |roll| = pi/2. Regime switch, +# not a fitted quantity: the recorded roll settles to 1.5708 +- 0.0001 within +# one action of touching down, so anything in [0.002, 0.02] selects the same +# steps. Kept a constant to keep the fitted parameter set minimal. +_FLAT_TOL = 0.01 + +# Actions of wind after switch-on before the fan cuts out. NOT identified - +# see DECISION RECORD. Set beyond the episode horizon so it is a no-op here. +_BLOW_DURATION_STEPS = 400 + + +def _wind_dir(observation, fan): + """Unit wind direction in the world plane, from the fan's own frame.""" + rot = observation.get(fan, "rot") + ux, uy = float(np.cos(rot)), float(np.sin(rot)) + # `facing_side` selects which side of its housing the fan blows from. + if observation.get(fan, "facing_side") > 0.5: + ux, uy = -ux, -uy + return ux, uy + + +def wind_rule(observation, latent, history, updates, params, cmds): + """Wind from every running fan, on every free domino.""" + fans = [o for o in observation.data if o.type.name == "fan"] + dominoes = [o for o in observation.data if o.type.name == "domino"] + for fan in fans: + fl = latent.setdefault(fan.name, {}) + if observation.get(fan, "is_on") <= 0.5: + fl["blow_steps"] = 0 + continue + elapsed = fl.get("blow_steps", 0) + fl["blow_steps"] = elapsed + 1 + if elapsed >= _BLOW_DURATION_STEPS: + continue + ux, uy = _wind_dir(observation, fan) + force = params["wind_force"] + torque = params["topple_torque"] + creep = params["creep_speed"] + for dom in dominoes: + if observation.get(dom, "is_held") > 0.5: + continue + roll = observation.get(dom, "roll") + if abs(abs(roll) - 0.5 * np.pi) < _FLAT_TOL: + # Regime B: already down - steady downwind creep. + cmds.set_velocity(dom, linear=(creep * ux, creep * uy, 0.0)) + else: + # Regime A: standing (or mid-fall) - push + topple moment. + cmds.apply_force(dom, (force * ux, force * uy, 0.0)) + cmds.apply_torque(dom, (-torque * uy, torque * ux, 0.0)) + return updates + + +LATENT_INIT = {} + +RESIDUAL_RULES = [wind_rule] + +PARAM_SPECS = [ + # Net downwind force on a standing/tipping domino (N). + ParamSpec("wind_force", 0.1504, lo=0.03, hi=0.35), + # Toppling moment about (z_hat x wind_dir) (N*m); the part of the wind + # load that acts above the COM on the tall exposed face. + ParamSpec("topple_torque", 0.0235, lo=0.004, hi=0.08), + # Steady creep speed of a fallen domino (m/s in engine units). + ParamSpec("creep_speed", 0.0260, lo=0.004, hi=0.08), + # --- predicate-only geometry (no SSE signal; see DECISION RECORD) --- + # Downwind displacement from the standing launch pose to where the block + # ends up at the end of a TurnFanOn rollout (topple arc + creep). + # Measured: 0.7012 - 0.57275 = 0.1285 in the real data. + ParamSpec("launch_offset", 0.1285, lo=0.05, hi=0.25), + # Half-width of the acceptable launch band along the wind axis. + ParamSpec("launch_tol", 0.018, lo=0.002, hi=0.05), + # Half-width of the acceptable lateral (cross-wind) launch band. + ParamSpec("launch_lat_tol", 0.06, lo=0.01, hi=0.1), + # Max |roll| still counted as "standing". + ParamSpec("upright_roll_max", 0.09, lo=0.02, hi=0.3), + # Min |roll| counted as "knocked flat". + ParamSpec("flat_roll_min", 1.40, lo=0.5, hi=1.55), + # Max deviation of the broad face from square-on to the wind (rad). + ParamSpec("face_to_wind_tol", 0.25, lo=0.05, hi=0.7), +] + +RESIDUAL_FEATURES = {"domino": ["x", "z", "roll"]}

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