标题： CPED-NCBFs：基于专家演示的神经控制屏障函数的保角预测 显示英文标题

标题： CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions

摘要： 在强化控制系统安全性的有前途的方法中，从专家演示中学习控制屏障函数（CBFs）已成为一种有效策略。然而，一个关键挑战仍然存在：验证所学的CBFs是否真正在整个状态空间中确保安全性。当CBF使用神经网络（NCBFs）表示时，这尤其困难。现有的几种验证技术试图解决这个问题，包括基于SMT的求解器、混合整数规划（MIP）和区间或边界传播方法，但这些方法通常引入松散、保守的边界。为了克服这些限制，本文我们使用CPED-NCBFs，一种基于分割共形预测的验证策略，来验证从专家演示中学习到的NCBF。我们进一步在质点系统和自行车模型上验证了我们的方法，以展示所提出理论的有效性。 显示英文摘要

摘要： 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.