Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation

Summary

Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions.

Abstract Summary

Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions. Existing methods typically avoid broad evaluation of contact-mode sequences through search heuristics or optimization reformulations. We revisit broad evaluation in light of modern GPU hardware and introduce Contact-Mode Expansion with parallel Trajectory optimization (CoMET), which combines GPU-parallel trajectory evaluation with greedy contact-mode expansion. On planar pushing benchmarks, CoMET is competitive with optimization-based, sampling, and tree-search baselines in solution quality and planning time, matching the full-enumeration reference on nearly all instances with fewer evaluations and shorter planning times. Ablations suggest that much of the performance gain comes from the high-throughput trajectory evaluator. In bimanual nonprehensile manipulation, GPU-friendly local mode expansion achieves higher planning success than the tested adaptive tree search as the mode space grows. These results demonstrate that broad explicit mode evaluation provides a simple yet effective alternative for CRMP.

Key Points

Contact-rich motion planning (CRMP) is essential for robotic manipulation and locomotion, yet remains computationally challenging due to combinatorial contact decisions.
Existing methods typically avoid broad evaluation of contact-mode sequences through search heuristics or optimization reformulations.
We revisit broad evaluation in light of modern GPU hardware and introduce Contact-Mode Expansion with parallel Trajectory optimization (CoMET), which combines GPU-parallel trajectory evaluation with greedy contact-mode expansion.
On planar pushing benchmarks, CoMET is competitive with optimization-based, sampling, and tree-search baselines in solution quality and planning time, matching the full-enumeration reference on nearly all instances with fewer evaluations and shorter planning times.
Ablations suggest that much of the performance gain comes from the high-throughput trajectory evaluator.

Method

CoMET combines parallel contact-mode evaluation with mode expansion (Fig. 2 ). Each candidate specifies a contact-mode order, while continuous optimization determines the trajectory, contact interactions, and transition timing.

Starting from a broad seed batch, CoMET evaluates candidates in parallel under a limited optimization budget and expands the neighborhoods of orders ranked by a merit function combining objective value and constraint violation. The leading feasible candidates are then refined to obtain the final plan.

Method — Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation — Fig. 5: Visualization of a planned L-block flip, with the arms placed by inverse kinematics at the planned paddle poses. Labels give the block angle and the two paddle contact-face assignments; “–” denotes free motion.

Experiments

We evaluate high-throughput contact-mode planning in two regimes. Planar pushing compares against existing planners and full enumeration, with controls separating the effects of the TO evaluator, GPU execution, and discrete search allocation.

Bimanual flipping tests search allocation when the mode space greatly exceeds the evaluation budget, using the same TO evaluator and budget across policies, and validates the resulting plans on hardware.

Sources and Demonstrations

Figures are reproduced from Li et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.