Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior

• URL: http://arxiv.org/abs/2609.06591v1
• Date: Sun, 06 Sep 2026 13:16:48 GMT
• Title: Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior
• Abstract summary: CHIP is a physics-grounded framework for learning reusable humanoid interaction skills from heterogeneous motion data.<n>We first learn physics-based motion-imitation policies that acquire grounded teacher behaviors from heterogeneous interaction data.<n>We then distill these behaviors into a context-conditioned interaction prior that captures reusable motion structure across locomotion and manipulation.
• Score: 20.58647951068921
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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