论文标题
通过投影对状态安全的情节学习的控制障碍观点
A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
论文作者
论文摘要
在本文中，我们旨在量化学习障碍功能（CBF）赋予安全保证的学习能力。特别是，我们研究了如何通过学习来减少CBF的时间导数的模型不确定性，以及这如何导致对系统安全行为的更强烈的陈述。为此，我们基于投入到国家安全（ISSF）以定义投影对国家安全（PSSF）的想法，该预测安全性在预计的干扰方面的降级为特征。这可以直接量化学习方式可以改善安全保证，以及学习错误的界限如何转化为安全性降解的界限。我们证明，实用的情节学习方法可以使用PSSF来减少不确定性并提高模拟和实验性的安全保证。
In this paper we seek to quantify the ability of learning to improve safety guarantees endowed by Control Barrier Functions (CBFs). In particular, we investigate how model uncertainty in the time derivative of a CBF can be reduced via learning, and how this leads to stronger statements on the safe behavior of a system. To this end, we build upon the idea of Input-to-State Safety (ISSf) to define Projection-to-State Safety (PSSf), which characterizes degradation in safety in terms of a projected disturbance. This enables the direct quantification of both how learning can improve safety guarantees, and how bounds on learning error translate to bounds on degradation in safety. We demonstrate that a practical episodic learning approach can use PSSf to reduce uncertainty and improve safety guarantees in simulation and experimentally.
