文脈条件付き相互作用事前分布による物理ベース人型ロボットの統合的操作
Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior
本論文は、歩行と物体操作を統合する物理ベースの人型ロボット制御フレームワークCHIPを提案し、異種の動作データから再利用可能なスキルを学習する。
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著者: Jianan Li, Xiao Chen, Tien-Tsin Wong
分類: cs.RO, cs.GR
原文アブストラクト
Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Existing approaches are often specialized for either locomotion or object-centric manipulation, or rely on task-specific reward engineering that does not scale well across diverse behaviors. We present CHIP, a unified, physics-grounded framework for learning reusable humanoid interaction skills from heterogeneous motion data. Central to our approach is a conditional interaction prior that models a context-dependent distribution over these skills within a shared discrete space. Our method is trained in three stages. We first learn physics-based motion-imitation policies that acquire grounded teacher behaviors from heterogeneous interaction data. We then distill these behaviors into a context-conditioned interaction prior that captures reusable motion structure across locomotion and manipulation. Finally, we initialize downstream task policies from the pretrained prior and adapt them through prior-regularized online RL post-training. Experiments on a diverse suite of humanoid interaction tasks show that our approach supports scene-aware locomotion, contact-rich object manipulation, and compositional behaviors such as environment-aware object transport and long-horizon skill sequencing, while producing smooth transitions and physically plausible motion.
