DynaWM: Dynamics-Aware Distillation with World Model and Momentum Targets for Smooth Locomotion over Continuous Stairs

The article introduces DynaWM, a dynamics-aware representation learning framework designed to improve bipedal-wheeled robots' ability to traverse continuous stairs. Key innovations include the incorporation of a world model as a regularizer for enhanced terrain encoding and a momentum target encoder to stabilize knowledge transfer during distillation. Experimental results indicate that DynaWM significantly improves terrain adaptability and motion smoothness, making it relevant for practitioners focused on advancing robotic locomotion in complex environments.