让机器人像人一样柔顺应对外力，从示范动作中学习全身控制。
SoftMimic: Learning Compliant Whole-body Control from Examples
- 用逆运动学生成柔顺动作数据，训练强化学习策略。
- 单段示范动作即可泛化到多种任务，抗干扰能力强。
- 适合需要安全人机交互的机器人场景，如协作或复杂环境操作。
我们提出SoftMimic，一种从示范动作中学习拟人机器人柔顺全身控制策略的框架。传统基于强化学习的模仿方法倾向于产生刚性控制，对参考轨迹过度修正，导致在遭遇意外接触时行为脆弱且不安全。相比之下，SoftMimic通过逆运动学求解器生成可行的柔顺动作数据集，训练强化学习策略以匹配柔顺响应而非严格追踪参考轨迹，从而实现对外部力的主动吸收和姿态稳定。该方法仅需一个动作片段即可实现多任务泛化，在仿真与真实实验中均验证了其在环境交互中的安全性与有效性。
原文摘要 · Abstract (English)
We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with reinforcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that aggressively corrects deviations from a reference motion, leading to brittle and unsafe behavior when the robot encounters unexpected contacts. In contrast, SoftMimic enables robots to respond compliantly to external forces while maintaining balance and posture. Our approach leverages an inverse kinematics solver to generate an augmented dataset of feasible compliant motions, which we use to train a reinforcement learning policy. By rewarding the policy for matching compliant responses rather than rigidly tracking the reference motion, SoftMimic learns to absorb disturbances and generalize to varied tasks from a single motion clip. We validate our method through simulations and real-world experiments, demonstrating safe and effective interaction with the environment.
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