ImpedanceMimic: Learned Impedance as Action Space for Force-Accurate Motion Imitation

Recovering the forces behind a motion is ill-posed: different controllers can reproduce the same movement while exerting different contact forces. Most motion-imitation methods anchor the humanoid in a physics simulator that grounds the contact dynamics, and drive it with a fixed-gain proportional–derivative (PD) controller. We show that fixed gains enforce a compromise between tracking the motion and recovering the resulting forces, where contact and free swing place opposing demands on the gains. To overcome this limitation, we present ImpedanceMimic, a motion-imitation reinforcement-learning policy that outputs per-joint stiffness, damping and position setpoints, supervised by force measurements withheld at inference. Across 129 participants, nine datasets and six diverse activities from lunges to jumping, ImpedanceMimic outperforms fixed-gain PD, direct torque and PHC on kinematic and force fidelity alike, and approaches a dedicated motion-to-force regressor. With gains learned as multiples of inertia-scaled priors, our policy is the first to drive subject-scaled biomechanical models for every participant, without anthropometric input, and transfers zero-shot to new bodies. The learned impedance forms an activity-specific, morphology-invariant signature of the underlying motor strategy, allowing the policy to generalize to unseen activities. This signature occupies a low-dimensional, interpretable space where stiffness scales with contact force and damping anticipates contact, consistent with human perturbation studies. We release our code, model checkpoints and benchmarks at https://impedancemimic.github.io/ImpedanceMimic.