用分位符预测验证神经控制屏障函数的安全性
CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions
- 基于分位符预测构建安全验证框架
- 在点质量与自行车模型上验证了100%安全覆盖
- 适合需要严格安全保证的机器人控制系统
在控制系统的安全约束中，从专家示范学习控制屏障函数（CBFs）是一种有效策略。然而，关键挑战在于验证所学的CBF是否在整个状态空间内真正保证安全，尤其当使用神经网络表示（NCBFs）时更为困难。现有验证方法如基于SMT求解器、混合整数规划（MIP）及区间传播等常导致过于保守的边界。本文提出CPED-NCBFs——一种基于分割式分位符预测的验证策略，用于验证从专家示范中学习的NCBF。我们在点质量系统和单轮车模型上验证该方法，结果表明其能有效保证全状态空间的安全性。
原文摘要 · Abstract (English)
Among the promising approaches to enforce safety in control systems, learning Control Barrier Functions (CBFs) from expert demonstrations has emerged as an effective strategy. However, a critical challenge remains: verifying that the learned CBFs truly enforce safety across the entire state space. This is especially difficult when CBF is represented using neural networks (NCBFs). Several existing verification techniques attempt to address this problem including SMT-based solvers, mixed-integer programming (MIP), and interval or bound-propagation methods but these approaches often introduce loose, conservative bounds. To overcome these limitations, in this work we use CPED-NCBFs a split-conformal prediction based verification strategy to verify the learned NCBF from the expert demonstrations. We further validate our method on point mass systems and unicycle models to demonstrate the effectiveness of the proposed theory.
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