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This paper introduces GRASP, a novel optimization framework for physics-based motion retargeting in robots that leverages gradient guidance from archived samples without needing a differentiable simulator. By combining sampling exploration with Markov chain Monte Carlo refinement, the method achieves high success rates on complex dexterous tasks like bimanual manipulation and humanoid locomotion. The approach significantly advances robot learning by enabling accurate reproduction of contact-rich human demonstrations using standard simulators.
GRASP: Gradient-Guided Sampling for Contact-Rich Physics-Based Motion Retargeting
arXiv:2611.05754v1 Announce Type: new 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.
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.
26 pages, including references and appendices. Project website: this https URL
Robotics (cs.RO)
arXiv:2611.05754 [cs.RO]
(or arXiv:2611.05754v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.05754 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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