arXiv:2606.00374v2 Announce Type: replace Abstract: 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, we enable the satisfaction of arbitrary runtime constraints on both the kinematic reference motion and the underlying dynamics, with efficient computation at frequencies above 1 kHz. In whole-body motion-tracking experiments in simulation and hardware, 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. Project page: https://stanfordasl.github.io/constrainedmimic.