SoftMimic: Learning Compliant Whole-body Control from Examples
This work improves robot safety and adaptability for humanoid applications by enabling compliant responses to external forces, though it is incremental as it builds on prior imitation learning and reinforcement learning techniques.
The paper tackled the problem of learning compliant whole-body control for humanoid robots from example motions, addressing the issue of stiff and brittle behavior in existing methods, and demonstrated safe and effective interaction in simulations and real-world experiments.
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.