DexSynRefine: 物理的に実現可能な器用なロボット動作のための人間と物体のインタラクション動作の合成と洗練
DexSynRefine: Synthesizing and Refining Human-Object Interaction Motion for Physically Feasible Dexterous Robot Actions
人間と物体のインタラクション動作を構造化された動作事前分布として扱い、タスク空間残差強化学習と接触ダイナミクス適応により物理的に実現可能な器用なロボット動作を生成するフレームワークを提案。実世界成功率を大幅に向上させた。
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著者: Hyesung Lee, Hyunwoo Jung, Si-Hwan Heo, Sungwook Yang
分類: cs.RO
原文アブストラクト
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.
