Stubborn: A Streamlined and Unified Reinforcement Learning Framework for Robust Motion Tracking and Fall Recovery for Humanoids

The article presents Stubborn, a unified reinforcement learning framework designed for robust humanoid motion tracking and fall recovery, utilizing an asymmetric Actor-Critic architecture. Key innovations include a yaw-aligned tracking representation to minimize drift, a Bernoulli-based probabilistic termination mechanism for enhanced exploration of recovery behaviors, and a dynamic sampling strategy that adapts to tracking performance. This framework addresses the limitations of existing methods by integrating motion tracking and recovery into a single training process, which is crucial for practitioners seeking to improve humanoid robot resilience in real-world environments.