从离线数据训练保守型神经控制屏障函数，提升系统安全性。
Learning Conservative Neural Control Barrier Functions from Offline Data
- 基于离线数据训练神经控制屏障函数，避免危险状态
- 新方法使系统远离分布外状态，增强可靠性
- 适合需要高安全性的控制系统设计者
安全滤波器，特别是基于控制屏障函数的方法，已成为保障动态系统安全控制的有效工具。然而，现有的可保证正确性的合成算法面临维度灾难问题。近年来，深度学习方法被提出以缓解此挑战。本文提出一种从离线数据集训练神经控制屏障函数的算法。这些函数可用于构建二次规划中的约束，进而作为安全滤波器。该算法训练时不仅阻止系统进入危险状态，还使其回避分布外状态（此时模型可靠性下降）。其灵感源自保守Q-learning（Conservative Q-learning）这一离线强化学习算法。我们称其输出为保守控制屏障函数（CCBFs）。实验表明，CCBFs在保持安全性的同时，对任务性能影响最小，优于现有方法。源代码见：https://github.com/tabz23/CCBF。
原文摘要 · Abstract (English)
Safety filters, particularly those based on control barrier functions, have gained increased interest as effective tools for safe control of dynamical systems. Existing correct-by-construction synthesis algorithms for such filters, however, suffer from the curse-of-dimensionality. Deep learning approaches have been proposed in recent years to address this challenge. In this paper, we add to this set of approaches an algorithm for training neural control barrier functions from offline datasets. Such functions can be used to design constraints for quadratic programs that are then used as safety filters. Our algorithm trains these functions so that the system is not only prevented from reaching unsafe states but is also disincentivized from reaching out-of-distribution ones, at which they would be less reliable. It is inspired by Conservative Q-learning, an offline reinforcement learning algorithm. We call its outputs Conservative Control Barrier Functions (CCBFs). Our empirical results demonstrate that CCBFs outperform existing methods in maintaining safety while minimally affecting task performance. Source code is available at https://github.com/tabz23/CCBF.
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