CoDance：从视频中学习反应性与顺从性的人-人形机器人交互
CoDance: Learning Reactive and Compliant Human-Humanoid Interaction from Video
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中文总结 AI 辅助
CoDance从视频中学习人形机器人与人类伙伴的反应性和顺从性交互，通过多连杆顺从性增强训练策略，在仿真中再现约80%腕部位移，并在物理机器人上实现持续双手舞蹈。
中文摘要 AI 辅助
合作式的人-人形机器人交互将运动与持续的身体接触相结合。人形机器人需要与人协调运动，同时响应交互力并保持稳定自然的动作。我们提出了CoDance，一个从视频中学习反应性与顺从性的人-人形机器人交互的框架。我们将合作舞蹈作为一个具有挑战性的实例进行研究，其中人形机器人与移动的伙伴协调脚步并保持双手持续接触。给定一段包含两名人类舞者的视频，CoDance将他们的动作重定向为机器人参考动作和移动伙伴的动作。我们引入了一种多连杆顺从性增强方法，通过在双手施加结构化力的条件下调整机器人参考动作，将运动学演示转化为力感知的训练数据。基于这些数据训练的策略能够跟随观察到的伙伴，同时保留演示的运动风格，并对物理交互做出顺从的响应。在仿真中，策略能够适应伙伴的变化调整脚步，并再现增强演示所编码的约80%的腕部位移。在物理人形机器人上，CoDance实现了与人类伙伴的持续双手舞蹈，包括前后运动之间的重复转换。
英文摘要
Partnered human-humanoid interaction couples locomotion with continuous physical contact. A humanoid needs to coordinate with a person's motion while responding to interaction forces and maintaining stable and natural movement. We present CoDance, a framework for learning reactive and compliant human-humanoid interaction from video. We study partnered dancing as a challenging instantiation, where a humanoid coordinates its footsteps with a moving partner and maintains continuous two-hand contact. Given a single video of two human dancers, CoDance retargets their motions into a robot reference and a moving partner. We introduce a multi-link compliance augmentation that transforms the kinematic demonstration into force-aware training data by adapting the robot reference under structured forces at both hands. Policies trained on this data follow the observed partner while preserving the demonstrated locomotion style and responding compliantly to physical interaction. In simulation, the policies adapt their footsteps to changes in the partner and reproduce approximately 80% of the wrist displacement encoded by the augmented demonstrations. On a physical humanoid, CoDance enables sustained two-hand dancing with a human partner including repeated transitions between forward and backward motions.
发表机构
- Duke University（杜克大学）
机构由 AI 辅助整理，请以论文原文为准。
