Mixture-Of-Experts Policy for Smooth and Stable Multi-Posture Fall Recovery in Bipedal Robot

Abstract

Bipedal robots are inherently prone to falling due to their higher center of mass and narrower support polygon, making automatic fall recovery a long-standing challenge. Existing approaches often rely on posture-specific strategies or exhibit limited robustness and generalization, restricting their real-world applicability. We present a unified Mixture-of-Experts (MoE) framework that trains a single policy capable of recovering from diverse fallen configurations. By leveraging base height estimation and proprioceptive history within a gating mechanism, the framework dynamically allocates recovery tasks to specialized experts, yielding smooth and stable motions. Extensive real-world experiments show that the policy transfers zero-shot to hardware and consistently achieves recovery not only under repeated disturbances, but also from highly challenging postures and even on inclined slopes—demonstrating robustness and generalization beyond prior methods.