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arXiv · 2611.05754

GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting

Yang Liu·Tong Wang·Yiming Bao·Heng Yang
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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.

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Yang Liu, Tong Wang, Yiming Bao, Heng Yang. 2026-11-03. GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting. https://arxiv.org/abs/2611.05754

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