CPED-NCBFs: 専門家デモンストレーションに基づくニューラル制御バリア関数のための共形予測
CPED-NCBFs: A Conformal Prediction for Expert Demonstration-based Neural Control Barrier Functions
専門家のデモから学習したニューラル制御バリア関数の安全性を、分割共形予測を用いて検証する手法を提案し、質点系と一輪車モデルで有効性を示した。
詳しい要約
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著者: Sumeadh MS, Kevin Dsouza, Ravi Prakash
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
