SOURCE-LINKED INTELLIGENCE
Contact-Rich Motion Planning via GPU-Parallel Mode Evaluation
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 optimizati
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-18T14:16:31.000Z
First collected: 2026-09-23T13:51:27.104Z. This is not the publication date.