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

2026-09-06

AI Summary

本文提出CHIP框架，通过学习异构运动数据中的可重用技能来解决统一物理基础的人形机器人控制问题，适用于复杂3D场景导航和物体操作。

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.

Problem

• unified physics-based humanoid controllers
• complex 3D scenes
• object manipulation
• task-specific reward engineering
• diverse behaviors

Innovation

• unified physics-based framework
• conditional interaction prior
• heterogeneous motion data
• context-dependent distribution
• prior-regularized online RL