制御バリア関数と状態射影安全を用いた二足歩行のエピソード学習
Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safety
制御バリア関数と機械学習を組み合わせ、モデル不確かさを学習して二足歩行ロボットの安全な動的歩行を実現する手法を提案し、シミュレーションと実機で検証した。
著者: Noel Csomay-Shanklin, Ryan K. Cosner, Min Dai, Andrew J. Taylor, Aaron D. Ames
分類: cs.RO
原文アブストラクト
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.
