Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control

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 technique...

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Vydáno v:IEEE transactions on robotics Ročník 39; číslo 3; s. 1749 - 1767
Hlavní autoři: Dawson, Charles, Gao, Sicun, Fan, Chuchu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Asymptotic stability
Certificates
Control systems
Deep learning in robotics and automation
formal methods in robotics and automation
Learning theory
Lyapunov methods
Measurement
neural certificates
robot safety
Robotics
Robots
Safety
Training
Trajectory
Trajectory tracking
ISSN:1552-3098, 1941-0468
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Shrnutí: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 article, we provide a comprehensive survey of this rapidly developing field of certificate learning. We hope that this article 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.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2022.3232542