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Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods | HTML5

ar5iv.labs.arxiv.org · 23,048 words · saved by 1 readers

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 t

Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction Methods for Robotics and Control Charles Dawson, Sicun Gao, and Chuchu Fan C. Dawson and C. Fan are with the Department of Aeronautics and Astronautics at the Massachusetts Institute of Technology. This work was supported by the Defense Science and Technology Agency in Singapore, but this article solely reflects the opinions and conclusions of its authors and not DSTA Singapore or the Singapore Government. C. Dawson is supported by the NSF GRFP under Grant No. 1745302.S. Gao is with the Department of

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