学習された証明書による安全制御：ニューラルリアプノフ・バリア・収縮法のサーベイ
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods
制御方策と同時に証明書を学習することで安全性や安定性を保証する手法（ニューラルリアプノフ・バリア・収縮法）の理論と実践を包括的に解説したサーベイ論文。
著者: Charles Dawson, Sicun Gao, Chuchu Fan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Learning-enabled control systems have demonstrated impressive empirical performance on challenging control problems in robotics, but this performance comes at the cost of reduced transparency and lack of guarantees on the safety or stability of the learned controllers. In recent years, new techniques have emerged to provide these guarantees by learning certificates alongside control policies -- these certificates provide concise, data-driven proofs that guarantee the safety and stability of the learned control system. These methods not only allow the user to verify the safety of a learned controller but also provide supervision during training, allowing safety and stability requirements to influence the training process itself. In this paper, we provide a comprehensive survey of this rapidly developing field of certificate learning. We hope that this paper will serve as an accessible introduction to the theory and practice of certificate learning, both to those who wish to apply these tools to practical robotics problems and to those who wish to dive more deeply into the theory of learning for control.
