统一基于物理的人形交互与上下文条件交互先验
Unifying Physics-Based Humanoid Interaction with a Context-Conditioned Interaction Prior
- The Chinese University of Hong Kong（香港中文大学）
- Monash University（莫纳什大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出CHIP框架，利用条件交互先验从异构数据学习统一人形交互技能，通过三阶段训练支持场景感知运动、物体操作及组合行为，产生平滑且物理合理的运动。
AI中文摘要：
开发能够导航复杂3D场景并操作物体的统一基于物理的人形控制器仍然是一个长期挑战。现有方法通常专门针对运动或物体中心操作，或依赖无法在多样行为中良好扩展的任务特定奖励工程。我们提出CHIP，一个统一的、基于物理的框架，用于从异构运动数据中学习可复用的人形交互技能。我们方法的核心是一个条件交互先验，它在共享离散空间内对这些技能建模上下文相关分布。我们的方法分三个阶段训练。首先，我们学习基于物理的运动模仿策略，从异构交互数据中获取有根据的教师行为。然后，我们将这些行为蒸馏到一个上下文条件交互先验中，该先验捕获运动和操作中可复用的运动结构。最后，我们从预训练先验初始化下游任务策略，并通过先验正则化的在线强化学习后训练进行适应。在多样的人形交互任务套件上的实验表明，我们的方法支持场景感知运动、接触丰富的物体操作以及组合行为，如环境感知物体运输和长视野技能序列，同时产生平滑过渡和物理上合理的运动。
英文摘要：
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.
