GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting
Overview
This research presents GRASP (Gradient Recovery from Archived Samples for Proposals), a method for physics-based motion retargeting that combines sampling exploration with gradient-guided refinement without needing a differentiable simulator.
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Reported quantity · demonstration count: 1352 other · Basis not reported
“eceding-horizon planning. Experiments on dexterous hands and humanoids show accurate retargeting with a shared tracking objective and no task-specific contact rewards, including 92.8% success on 1352 bimanual OakInk2 demonstrations. The refined references improve PPO tracking in pen spinning and humanoid-object interaction. We also demonstrate dexterous manipulation and whole-body humanoid motion on real hardware.”
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- 2026-11-07T03:53:21.015Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalGRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting
This research presents GRASP (Gradient Recovery from Archived Samples for Proposals), a method for physics-based motion retargeting that combines sampling exploration with gradient-guided refinement without needing a differentiable simulator.
Event coverage history
Current attention 3·Peak within the comparable range 10（2026-11-05 06:00 UTC）·Change within the comparable range over 24 hours -49%
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