Constrained Whole-Body Tracking for Humanoid Robots
This work addresses the challenge of enforcing safety constraints post-training in RL-based humanoid control, enabling practical deployment with minimal policy restriction.
ConstrainedMimic integrates operational space control and control barrier functions to enforce runtime constraints (collision avoidance, joint limits, stability) in reinforcement learning-based whole-body tracking for humanoid robots, achieving real-time deployment at 300-500 Hz on a simulated Unitree G1.
Recent advances in reinforcement learning (RL) have demonstrated impressive whole-body agility for humanoid robots, yet ensuring safety and satisfying constraints -- particularly those specified after training -- remains a challenge. Towards this goal, we present ConstrainedMimic, a control framework that leverages whole-body kinematics and dynamics for real-time constraint enforcement within RL tracking policies. By integrating principles from operational space control and control barrier functions (CBFs), we enable the satisfaction of arbitrary runtime constraints on both the kinematic reference motion and the underlying dynamics. In whole-body motion-tracking and teleoperation experiments on a (simulated) Unitree G1 with a learned policy, we demonstrate collision avoidance (both with the robot body and external obstacles), joint limits, and center of mass stability constraints. By remaining consistent with the current contact mode and tracking objectives, we minimally restrict the capabilities of the policy when constraints are active. Our method is fully differentiable, runs on CPU, GPU, and TPU, and can be deployed at up to 300-500 Hz. All software will be freely available upon publication.