diff --git a/papers/mcvamp/index.html b/papers/mcvamp/index.html index 542b99b7..f1151a68 100644 --- a/papers/mcvamp/index.html +++ b/papers/mcvamp/index.html @@ -58,9 +58,10 @@ Grasp Diffusion - + GPU RRT-Connect + GPU-Accelerated Kinodynamic Planning diff --git a/papers/pRRTC/index.html b/papers/pRRTC/index.html index e06923b2..f6c7403f 100644 --- a/papers/pRRTC/index.html +++ b/papers/pRRTC/index.html @@ -464,14 +464,18 @@
+ Sampling-based motion planners have been shown to be effective for systems with complex + kinodynamic constraints and high dimensionality. + However, these algorithms struggle to achieve real-time performance, leading to recent + efforts to parallelize planning. + While GPU-accelerated planners have achieved significant speedups, existing approaches + require specialized CUDA programming that limits accessibility and portability. +
++ We present Parallel Asymptotically Optimal Kinodynamic RRT (PAKR), a massively parallel + kinodynamic planner leveraging JAX and the XLA compiler to achieve GPU acceleration + through standard Python tooling. + By combining our parallel planner with the AO-x meta-algorithm, we achieve asymptotic + optimality through fast iterative replanning. +
++ We provide a theoretical analysis of probabilistic completeness, analyze the effects of + batch size and branching factor on convergence, and demonstrate scalability to complex + dynamics using the MuJoCo-XLA simulator. + Experiments show competitive runtimes with state-of-the-art GPU planners and superior solution quality. +
++ Utilizing the Python-JAX jit compiler, we compile the RRT planner into a single GPU kernel, minimizing device transfer overhead. + The planner performs node selection, action sampling, forward simulation, and tree building in large batches, enabling efficient parallelism. + With a fast single-solve time, we can iteratively replan with decreasing cost thresholds to achieve asymptotic optimality at high frequencies. +
++ These environments were taking from KinoPAX, a CUDA implementation of accelerated kinodynamic planning. + Three dynamics models are used: 6d double integrator, 6d dubins airplane, and 12d quadrotor. +
+
+ (a) Initial Tree
+
+ (b) Initial Narrow
+
+ (c) Initial House
+
+ (d) Final Tree
+
+ (e) Final Narrow
+
+ (f) Final House
++ Depiction of 10 initial and final solution trajectories (green lines) and 3d obstacle + environments for PAKR, run with the double integrator model. The start and goal regions are represented + by the blue and green spheres. Cost criterion is set to distance traveled to better visualize convergence. +
+ ++ To compare against optimal planners, we also evaluate against DynoBench's iDb-A* and SST* implementations. + Four dynamics models are used: 3d unicycle with velocity control, 5d unicycle with acceleration control, + 4d acrobot, and 12d quadrotor. +
+
+ 4d acrobot problem visualized in mujoco.
++ We also run our planner with the MuJoCo-XLA simulator, which provides GPU-accelerated physics simulation for + complex dynamics. With MJX, we solve a 4d cartpole problem and a 10d block push problem. In addition, we + use a soft growing vine simulator to demonstrate the ability of PAKR to handle complex, high-dimensional dynamics. +
+10d blockpush problem. Left has an easier start position.
+Soft growing vine planning problem
++ We evaluate the performance of PAKR against KinoPAX, a gpu-accelerated planner, and two optimal planners: iDb-A* and SST*. + We also analyze the convergence behavior of PAKR with respect to iterations and time, demonstrating its + ability to find high-quality solutions efficiently. +
+
+
+ | + | + | Env A | +Env B | +Env C | +|||
|---|---|---|---|---|---|---|---|
| Dynamics | +Planner | +Time | +Nodes | +Time | +Nodes | +Time | +Nodes | +
| 6D DI | +KinoPAX | +5.0ms | 164k | +3.5ms | 144k | +6.3ms | 193k | +
| PAKR | +2.5ms | 9k | +1.8ms | 21k | +5.5ms | 19k | +|
| 6D DA | +KinoPAX | +4.4ms | 63k | +3.7ms | 72k | +7.6ms | 107k | +
| PAKR | +5.4ms | 19k | +5.9ms | 32k | +14.9ms | 57k | +|
| 12D QC | +KinoPAX | +17.8ms | 285k | +17.8ms | 285k | +24.6ms | 333k | +
| PAKR | +14.9ms | 71k | +17.1ms | 147k | +51.4ms | 173k | +|
| + | Environment A | +Environment B | +Environment C | +|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dynamics | +Time1 | Cost1 | Timef | Costf | +Time1 | Cost1 | Timef | Costf | +Time1 | Cost1 | Timef | Costf | +
| 6D DI | +2.3ms | 1.76 | 28.0ms | 1.51 | +1.8ms | 2.03 | 20.0ms | 1.54 | +4.9ms | 3.11 | 45.0ms | 2.33 | +
| 6D DA | +4.8ms | 1.71 | 22.0ms | 1.56 | +5.9ms | 1.69 | 6.0ms | 1.66 | +14.8ms | 3.42 | 32.0ms | 2.98 | +
| 12D QC | +13.1ms | 2.16 | 20.1ms | 1.98 | +17.1ms | 2.42 | 31.0ms | 2.11 | +55.6ms | 3.98 | 165.4ms | 3.62 | +
| Problem | +Alg. | +SR% | +Time1 | +Cost1 | +Costf | +
|---|---|---|---|---|---|
| Unicycle 1 | +iDb-A* | 100 | 254ms | 5.80s | 5.10s | +
| SST* | 100 | 600ms | 8.34s | 4.41s | +|
| PAKR | 100 | 1.3ms | 4.62s | 4.46s | +|
| Unicycle 2 | +iDb-A* | 12 | 4637ms | 8.70s | 8.70s | +
| SST* | 100 | 398ms | 14.15s | 6.95s | +|
| PAKR | 100 | 1.4ms | 5.40s | 4.75s | +|
| Acrobot | +iDb-A* | 100 | 1450ms | 5.24s | 4.96s | +
| SST* | 74 | 3298ms | 4.36s | 3.39s | +|
| PAKR | 100 | 13ms | 3.87s | 3.22s | +|
| Quadcopter | +iDb-A* | 100 | 1387ms | 2.56s | 2.21s | +
| SST* | 41 | 85457ms | 9.64s | 9.42s | +|
| PAKR | 100 | 175ms | 7.54s | 6.23s | +
| + | + | Branching Factor (A) | +|||
|---|---|---|---|---|---|
| Batch | +Metric | +2 | 16 | 64 | 128 | +
| 4k | +Success (%) | 100.0 | 100.0 | 99.0 | 94.0 | +
| Time (ms) | 19.5 | 16.1 | 26.7 | 41.9 | |
| Iters | 22.0 | 36.0 | 69.0 | 104.0 | |
| Nodes | 37.6k | 57.7k | 107.5k | 168.1k | |
| 8k | +Success (%) | 100.0 | 100.0 | 97.0 | 93.0 | +
| Time (ms) | 24.5 | 13.1 | 19.0 | 26.7 | |
| Iters | 15.0 | 21.0 | 39.0 | 57.0 | |
| Nodes | 54.3k | 70.9k | 124.9k | 181.9k | |
| 16k | +Success (%) | 100.0 | 100.0 | 97.0 | 87.0 | +
| Time (ms) | 47.2 | 14.3 | 17.1 | 14.3 | |
| Iters | 12.0 | 14.0 | 25.0 | 25.0 | |
| Nodes | 89.9k | 103.8k | 162.7k | 163.3k | |
| 32k | +Success (%) | 100.0 | 100.0 | 99.0 | 86.0 | +
| Time (ms) | 115.3 | 26.9 | 20.1 | 19.9 | |
| Iters | 10.0 | 12.0 | 17.0 | 20.0 | |
| Nodes | 154.6k | 175.5k | 232.3k | 277.0k | |
@misc{gao2026fastasymptoticallyoptimalkinodynamic,
+ title={Fast Asymptotically Optimal Kinodynamic Planning via Vectorization},
+ author={Yitian Gao and Andrew Lu and Zachary Kingston},
+ year={2026},
+ eprint={2607.03987},
+ archivePrefix={arXiv},
+ primaryClass={cs.RO},
+ url={https://arxiv.org/abs/2607.03987},
+ }
+