The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control
Abstract
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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Cited by
Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
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Metadata
- Authors
- Dawson, C., Gao, S., & Fan, C.
- Venue
- IEEE Transactions on Robotics
- Published
- 2023-06
- Volume
- 39
- Issue
- 3
- Pages
- 1749-1767
- ISSN
- 1552-3098 (ISSN-L)
- 1941-0468
- OpenAlex
- W4315606134
- Cited by
- 269
- References
- 68
- FWCI
- 23.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dawson, C., Gao, S., & Fan, C. (2023). Safe Control With Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control. IEEE Transactions on Robotics. https://doi.org/10.1109/tro.2022.3232542
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