让机器人像人一样灵活跳舞，还能稳稳走路。
ExBody2: Advanced Expressive Humanoid Whole-Body Control
- 分两步训练：先用仿真数据生成合理动作，再微调真实机器人
- 能稳定执行走、蹲、舞等复杂动作，误差显著降低
- 适合想让机器人更自然动作的研究者和开发者
本文针对现实世界中类人机器人实现富有表现力且动态的全身运动难题，提出高级表达性全身控制（ExBody2）方法。该方法结合人类动捕数据与仿真数据训练全身追踪控制器，并成功部署于真实机器人。通过解耦全身速度追踪与身体关键点追踪，利用教师策略生成符合机器人运动学特性的中间数据，并自动剔除不可行动作。此两阶段方法使学生策略可实现在真实机器人的行走、下蹲与舞蹈等动作。实验发现，少量数据微调后追踪性能显著提升，但会牺牲部分其他动作的表现力。研究揭示了灵活性与特定动作精度间的权衡关系。
原文摘要 · Abstract (English)
This paper tackles the challenge of enabling real-world humanoid robots to perform expressive and dynamic whole-body motions while maintaining overall stability and robustness. We propose Advanced Expressive Whole-Body Control (Exbody2), a method for producing whole-body tracking controllers that are trained on both human motion capture and simulated data and then transferred to the real world. We introduce a technique for decoupling the velocity tracking of the entire body from tracking body landmarks. We use a teacher policy to produce intermediate data that better conforms to the robot's kinematics and to automatically filter away infeasible whole-body motions. This two-step approach enabled us to produce a student policy that can be deployed on the robot that can walk, crouch, and dance. We also provide insight into the trade-off between versatility and the tracking performance on specific motions. We observed significant improvement of tracking performance after fine-tuning on a small amount of data, at the expense of the others.
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