微分可能な安全臨界制御の学習：制御バリア関数による新規環境への汎化
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
ECBF-QPの最適化を微分可能層として深層学習に組み込み、高相対次数系でも前方不変性を保証しつつ新環境へ汎化できる安全臨界制御フレームワークを提案した。
著者: Hengbo Ma, Bike Zhang, Masayoshi Tomizuka, Koushil Sreenath
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Control barrier functions (CBFs) have become a popular tool to enforce safety of a control system. CBFs are commonly utilized in a quadratic program formulation (CBF-QP) as safety-critical constraints. A class $\mathcal{K}$ function in CBFs usually needs to be tuned manually in order to balance the trade-off between performance and safety for each environment. However, this process is often heuristic and can become intractable for high relative-degree systems. Moreover, it prevents the CBF-QP from generalizing to different environments in the real world. By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees. Finally, we validate the proposed control design with 2D double and quadruple integrator systems in various environments.
