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arXiv · 2605.05925

DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions

Hyesung Lee·Hyunwoo Jung·Si-Hwan Heo·Sungwook Yang
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Abstract

Learning dexterous manipulation from human-object interaction (HOI) data offers a scalable alternative to robot teleoperation, but HOI demonstrations are typically sparse and purely kinematic, making direct retargeting unreliable under embodiment mismatch and contact-rich dynamics. We present DexSynRefine, a coupled framework that treats HOI data as structured motion priors rather than executable robot actions. DexSynRefine first synthesizes hand-object trajectories conditioned on the task and initial object state using HOI Motion Manifold Flow Primitives (HOI-MMFP), a motion prior for coupled hand-object motion. It then physically grounds them with task-space residual reinforcement learning and adapts execution by inferring missing contact-dynamics context from proprioceptive history. Across five dexterous manipulation tasks, each stage addresses a complementary bottleneck: HOI-MMFP improves trajectory consistency and smoothness, task-space residuals provide the strongest grounding representation among the tested alternatives, and contact-dynamics adaptation enables robust real-world execution. Together, DexSynRefine improves real-world success rates over kinematic retargeting by 50-70~percentage points.

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Hyesung Lee, Hyunwoo Jung, Si-Hwan Heo, Sungwook Yang. 2026-06-17. DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions. https://arxiv.org/abs/2605.05925

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