SDWM: Learning Bipedal Locomotion via a Smooth Denoising World Model Method

Blind locomotion policies trained in simulation for unstructured terrains often suffer from significant performance degradation when deployed on real bipedal robots. A typical manifestation is the emergence of abnormal behaviors (e.g., sudden high-stepping motions) even on simple terrains like flat ground. This issue arises from environmental noise interference and the inability to reliably obtain certain critical state information (such as terrain heightmaps and linear velocities) through proprioception. Existing methods commonly attempt to address this problem by injecting large amounts of noise and learning explicit or implicit representations of the missing information. Nevertheless, excessive noise can severely restrict the potential performance of the policy. The more fundamental challenge lies in the fact that both explicit and implicit representations constructed solely from historical proprioceptive data are unable to accurately reconstruct key unobservable state variables. This inevitable estimation bias ultimately leads to abnormal behaviors. To address these challenges, we propose a bipedal training framework based on a Smoothed and Denoised World Model (SDWM), which explicitly mitigates the effects of observation noise, incomplete state information, and inaccurate reconstruction. We validate the effectiveness of SDWMthrough comparative tests conducted in simulation as well as indoor and outdoor realworld scenarios.