ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion
For humanoid locomotion control, ADP provides a more perturbation-resilient policy by directly enforcing dynamics priors, offering a practical improvement over existing motion prior methods.
Adversarial Dynamics Priors (ADP) improves humanoid locomotion robustness by regularizing dynamics features (e.g., CoM motion, contact forces) instead of kinematic features, achieving a 16.7% higher impulse threshold, 47.9% faster recovery, and 35.4% lower velocity tracking error compared to AMP.
In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based methods induce natural motion styles by imitating kinematic motion features, but they do not directly regularize dynamics features, such as CoM motion, centroidal momentum, contact forces, and contact states. To address this limitation, we replace kinematic motion-style feature with selected dynamics features extracted from locomotion trajectories as the target of adversarial regularization.To this end, we use trajectory optimization to construct a reference dataset and train a discriminator to evaluate whether policy-induced temporal windows are consistent with the resulting reference distribution.Without explicit motion tracking, ADP encourages policy rollouts to remain close to the reference support, even after perturbations. Experimental results show that, compared with AMP, the strongest baseline in our evaluation, ADP improves the $80\%$-success impulse threshold ($J_{80}$) by $16.7\%$, while reducing direction-averaged recovery time and velocity tracking error by $47.9\%$ and $35.4\%$, respectively.