基于GPU并行模式评估的接触丰富运动规划

中文摘要 AI 辅助

接触丰富运动规划（CRMP）对于机器人操作和 locomotion 至关重要，但由于组合接触决策，其计算仍然具有挑战性。现有方法通常通过搜索启发式或优化重构来避免对接触模式序列进行广泛评估。我们基于现代 GPU 硬件重新审视广泛评估，并引入接触模式扩展与并行轨迹优化（CoMET），该方法将 GPU 并行轨迹评估与贪心接触模式扩展相结合。在平面推动基准测试中，CoMET 在解质量和规划时间上与基于优化、采样和树搜索的基线方法相当，在几乎所有实例上以更少的评估和更短的规划时间匹配全枚举参考。消融研究表明，大部分性能提升来自高吞吐量轨迹评估器。在双臂非抓取操作中，随着模式空间的增长，GPU 友好的局部模式扩展比测试的自适应树搜索实现了更高的规划成功率。这些结果表明，广泛的显式模式评估为 CRMP 提供了一种简单而有效的替代方案。

英文摘要

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.

发表机构

• TU Darmstadt（达姆施塔特工业大学）
• Robotics Institute Germany (RIG)（德国机器人研究所）

机构由 AI 辅助整理，请以论文原文为准。