Zhong Jiangwei

Keep On Going: Learning Robust Humanoid Motion Skills via Selective Adversarial Training

Humanoid robots are expected to operate reliably over long horizons while executing versatile whole-body skills. Yet Reinforcement Learning (RL) motion policies typically lose stability under prolonged operation, sensor/actuator noise, and real world disturbances. In this work, we propose a Selectiv