让双人形机器人在真实世界中自然互动，实现拥抱跳舞等复杂协作。
Rhythm: Learning Interactive Whole-Body Control for Dual Humanoids
- 从人类动作数据生成可交互的机器人参考轨迹
- 用图结构奖励机制让双机器人学会耦合动力学控制
- 首次实现在真实机器人上稳定复现仿真中的复杂互动
实现多机器人系统在共享环境中的物理交互式全身控制对解锁复杂协作能力至关重要。尽管近期进展显著提升了单个机器人的敏捷性，但将个体能力拓展至物理耦合的双人形交互仍面临严重运动学不匹配和复杂接触动力学挑战。为此，我们提出Rhythm，首个统一框架，支持双人形系统在真实世界的复杂、物理合理交互部署。该框架包含三个核心组件：(1) 交互感知的动作重定向（IAMR）模块，从人类数据生成可行的人形交互参考；(2) 交互引导的强化学习（IGRL）策略，通过图结构奖励掌握耦合动力学；(3) 真实世界部署系统，实现双人形交互的鲁棒迁移。在真实Unitree G1机器人上的大量实验表明，该框架实现了稳健的交互式全身控制，成功将拥抱、舞蹈等多种行为从仿真迁移到现实世界。
原文摘要 · Abstract (English)
Realizing interactive whole-body control for multi-humanoid systems is critical for unlocking complex collaborative capabilities in shared environments. Although recent advancements have significantly enhanced the agility of individual robots, bridging the gap to physically coupled multi-humanoid interaction remains challenging, primarily due to severe kinematic mismatches and complex contact dynamics. To address this, we introduce Rhythm, the first unified framework enabling real-world deployment of dual-humanoid systems for complex, physically plausible interactions. Our framework integrates three core components: (1) an Interaction-Aware Motion Retargeting (IAMR) module that generates feasible humanoid interaction references from human data; (2) an Interaction-Guided Reinforcement Learning (IGRL) policy that masters coupled dynamics via graph-based rewards; and (3) a real-world deployment system that enables robust transfer of dual-humanoid interaction. Extensive experiments on physical Unitree G1 robots demonstrate that our framework achieves robust interactive whole-body control, successfully transferring diverse behaviors such as hugging and dancing from simulation to reality.
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