Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
This work addresses safety in robotic locomotion for hardware applications, though it is incremental as it builds on existing control barrier function methods.
The paper tackled the problem of ensuring safe bipedal locomotion despite model uncertainties by combining episodic learning with control barrier functions, achieving successful hardware demonstrations on the AMBER-3M robot for precise foot placement in stepping-stone scenarios.
This paper combines episodic learning and control barrier functions in the setting of bipedal locomotion. The safety guarantees that control barrier functions provide are only valid with perfect model knowledge; however, this assumption cannot be met on hardware platforms. To address this, we utilize the notion of projection-to-state safety paired with a machine learning framework in an attempt to learn the model uncertainty as it affects the barrier functions. The proposed approach is demonstrated both in simulation and on hardware for the AMBER-3M bipedal robot in the context of the stepping-stone problem, which requires precise foot placement while walking dynamically.