[Submitted on 3 Nov 2026]
Title:GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting
View a PDF of the paper titled GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting, by Yang Liu and 3 other authors
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Abstract:We study physics-based retargeting of human demonstrations for robot learning: finding robot controls that reproduce reference motions under contact dynamics. We propose GRASP (Gradient Recovery from Archived Samples for Proposals), which combines sampling-based exploration with gradient-guided refinement without requiring a differentiable simulator. GRASP-TO extends tempered sequential Monte Carlo by fitting local quadratic cost models to archived rollouts and using their gradients to guide Markov chain Monte Carlo rejuvenation. Metropolis-Hastings correction preserves the target distribution for each fixed proposal kernel, and a population analysis connects the recovered gradients to Gaussian smoothing. GRASP-MPC scales this optimizer to long demonstrations through warm-started receding-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.
Comments: 26 pages, including references and appendices. Project website: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2611.05754 [cs.RO]
(or arXiv:2611.05754v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.05754
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yang Liu [view email] [v1] Tue, 3 Nov 2026 23:53:22 UTC (28,746 KB)
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