DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions
For robotics researchers, this provides a scalable pipeline to generate executable dexterous manipulation policies from limited human demonstration data, addressing embodiment mismatch and contact-rich dynamics.
DexSynRefine synthesizes physically feasible dexterous robot actions from sparse human-object interaction demonstrations, achieving 50-70 percentage point improvement over kinematic retargeting across five manipulation tasks in simulation and real-world transfer.
Learning dexterous manipulation from human-object interaction (HOI) data is a scalable alternative to teleoperation, but HOI demonstrations are sparse and provide only kinematic motion that is not directly executable under embodiment mismatch and contact-rich dynamics. We present DexSynRefine, a framework with three coupled components: HOI-MMFP, a task- and object-initial-state-conditioned extension of motion manifold primitives that synthesizes coordinated hand-object trajectories from sparse HOI demonstrations; a task-space residual RL policy that physically grounds the synthesized reference while inheriting its kinematic structure; and a contact-and-dynamics adaptation module that enables sim-to-real transfer from proprioceptive history. Across five dexterous manipulation tasks spanning pick-and-place, tool use, and object reorientation, our task-space residual policy outperforms prior action-representation baselines in simulations and transfers to a real robot on all five tasks, improving over kinematic retargeting by 50-70 percentage points.