Article

Learning Object-Centric Motion Priors from Human Data for Robotic Dexterous Manipulation

The paper proposes learning to holistically predict future hand-object states from human-object interaction (HOI) datasets, using the predicted future object states as a general-purpose reward term for reinforcement learning, reducing reliance on task-specific reward engineering. The authors experiment on three manipulation tasks in simulation and the real world, reporting better success rates and generalization to novel objects than existing SOTA methods, and validate cross-embodiment compatibility by deploying skills to different robot hands. The work has been accepted as an oral paper at AAAI 2026.

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