ニューラルバリア：オンラインで認証可能な学習強化適応高次安全臨界制御
Neural Barriers: An Online Certifiable Learning-enhanced Adaptive High Order Safety Critical Control
Neural ODEとConformal Predictionを用いて外乱に適応し、不確実性が高いときは保守的に、データが増えると制御の保守性を低減しながら、時変モデル外乱下でも確率的な安全性保証を提供する制御バリア関数手法を提案した。
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著者: Lishuo Pan, Mattia Catellani, An Cao, Lorenzo Sabattini, Nora Ayanian
分類: cs.RO
原文アブストラクト
Control barrier functions are an effective model-based tool to formally certify the safety of a system. However, transferring their theoretical guarantees to real-world robotics systems requires high model fidelity. For example, payloads or wind disturbances can cause significant model perturbations to an aerial vehicle, leading to safety compromises. In this work, we propose a certifiable online learning-enhanced robust adaptive control barrier function, which adapts to disturbances using a Neural ODE and quantifies its adaptation uncertainty with conformal prediction. Our approach guarantees safety at all time under unknown time-varying model disturbances. It adopts a conservative strategy when the adaptation uncertainty is high; and efficiently adapts to reduce controller conservativeness as it receives more data. Our approach provides a provable safety guarantee with a probability bound under suitable Lipschitz smoothness assumptions on the underlying model and trajectory. These results demonstrate the potential of our method as a practical safety controller for robotics system operating under model perturbations.
