Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety

This paper combines episodic learning and control barrier functions in the\nsetting of bipedal locomotion. The safety guarantees that control barrier\nfunctions provide are only valid with perfect model knowledge; however, this\nassumption cannot be met on hardware platforms. To address this, we utilize the\nnotion of projection-to-state safety paired with a machine learning framework\nin an attempt to learn the model uncertainty as it affects the barrier\nfunctions. The proposed approach is demonstrated both in simulation and on\nhardware for the AMBER-3M bipedal robot in the context of the stepping-stone\nproblem, which requires precise foot placement while walking dynamically.\n