Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Humanoid robots show significant potential in daily tasks. However,
reinforcement learning-based motion policies often suffer from robustness
degradation due to the sim-to-real dynamics gap, thereby affecting the agility
of real robots. In this work, we propose a novel robust adversarial training
paradigm designed to enhance the robustness of humanoid motion policies in real
worlds. The paradigm introduces a learnable adversarial attack network that
precisely identifies vulnerabilities in motion policies and applies targeted
perturbations, forcing the motion policy to enhance its robustness against
perturbations through dynamic adversarial training. We conduct experiments on
the Unitree G1 humanoid robot for both perceptive locomotion and whole-body
control tasks. The results demonstrate that our proposed method significantly
enhances the robot's motion robustness in real world environments, enabling
successful traversal of challenging terrains and highly agile whole-body
trajectory tracking.
