Whole-Body Compliant Control via Learned Force-Regulation Modules

To operate safely and effectively around people, humanoid robots must control the forces they exchange with the world. Whereas classical impedance and admittance methods shape contact forces precisely, modern reinforcement-learning controllers typically forgo such regulation in favor of robustness and faithful command tracking. Incorporating impedance-style compliance into a performant humanoid whole-body controller remains an open problem. We present a framework for learned whole-body force regulation that approximates the behavior of impedance controllers using only a PD position-target interface and position encoders. The framework includes three mechanisms for force regulation: passive joint-angle compliance via noisy action perturbations, joint-angle force regulation via perturbation--action blending around a commanded pose, and task-space force regulation via reward-shaped tracking of both a virtual forcefield attractor and a commanded pose. All three share a residual actor--critic recipe, an internal model of proprioception and perturbations, and a policy-blending procedure that combines multiple experts. A 6-DOF body command, optional upper-body pose, and controllable compliance expose the controllers as reusable low-level modules that enable compliant interaction and object manipulation across standing, crouching, and walking. We demonstrate whole-body teleoperation in simulation and on Sprout, a 27-DOF bipedal humanoid.