予測制御バリア関数を用いた階層型安全臨界制御の学習
Learning for Layered Safety-Critical Control with Predictive Control Barrier Functions
低次元モデルで設計した制御バリア関数に、全次元モデルのロールアウトから学習した予測ロバスト項を加えることで、モデル間ギャップによる安全違反を防ぐ手法を提案し、3Dホッピングロボットで実証した。
著者: William D. Compton, Max H. Cohen, Aaron D. Ames
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Safety filters leveraging control barrier functions (CBFs) are highly effective for enforcing safe behavior on complex systems. It is often easier to synthesize CBFs for a Reduced order Model (RoM), and track the resulting safe behavior on the Full order Model (FoM) -- yet gaps between the RoM and FoM can result in safety violations. This paper introduces \emph{predictive CBFs} to address this gap by leveraging rollouts of the FoM to define a predictive robustness term added to the RoM CBF condition. Theoretically, we prove that this guarantees safety in a layered control implementation. Practically, we learn the predictive robustness term through massive parallel simulation with domain randomization. We demonstrate in simulation that this yields safe FoM behavior with minimal conservatism, and experimentally realize predictive CBFs on a 3D hopping robot.
