Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation

2026-09-18

AI Summary

研究通过GPU并行模式评估解决接触丰富运动规划的计算难题，提出CoMET方法，在平面推力基准测试中表现出色。

Abstract

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.

Problem

• contact-rich motion planning
• combinatorial contact decisions
• GPU-parallel

Innovation

• Contact-Mode Expansion
• parallel Trajectory optimization
• GPU-parallel
• contact-rich motion planning
• bimanual nonprehensile manipulation