论文标题
通过学习证书的安全控制：对神经Lyapunov，障碍和收缩方法的调查
Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods
论文作者
论文摘要
基于学习的控制系统在机器人技术中表现出令人印象深刻的经验表现，但是这种性能是以降低透明度和缺乏对学识渊博控制器的安全性或稳定性的保证为代价的。近年来，已经出现了新技术，以通过学习证书以及控制政策提供这些保证 - 这些证书提供了简洁的，数据驱动的证明，以保证学习的控制系统的安全性和稳定性。这些方法不仅允许用户验证学习控制器的安全性，还可以在培训期间提供监督，从而使安全性和稳定性要求能够影响培训过程本身。在本文中，我们对这一迅速发展的证书学习领域进行了全面的调查。我们希望本文能够对证书学习的理论和实践进行可访问的介绍，这是对那些希望将这些工具应用于实用机器人问题的人，以及希望更深入地深入研究控制理论的人。
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.
