SoftMimic: Learning Compliant Whole-body Control from Examples
Gabriel Margolis, Michelle Wang, Nolan Fey, Pulkit Agrawal
AI summary
Problem
Current motion-tracking policies for humanoids are overly stiff, causing brittle and unsafe behavior when encountering unexpected contacts or disturbances in real-world environments.
Approach
The framework uses an inverse kinematics solver to generate a dataset of feasible, compliant motion examples across various forces and stiffness levels, then trains a reinforcement learning policy to imitate these compliant responses while tracking the original reference.
Key results
- Significantly reduces peak collision forces compared to stiff baselines
- Generalizes a single reference motion to handle varying object sizes and misalignments
- Enables real-time modulation of interaction stiffness at deployment
- Demonstrates safe, compliant physical interaction on a real Unitree G1 humanoid
Why it matters
It enables safe, adaptable physical interaction for humanoids, accelerating their deployment in unstructured, human-populated environments.
Abstract
We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with rein- forcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that ag- gressively 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 bal- ance 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.