CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions

• URL: http://arxiv.org/abs/2503.17395v1
• Date: Tue, 18 Mar 2025 10:01:06 GMT
• Title: CP-NCBF: A Conformal Prediction-based Approach to Synthesize Verified Neural Control Barrier Functions
• Authors: Manan Tayal, Aditya Singh, Pushpak Jagtap, Shishir Kolathaya,
• Abstract summary: Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers.<n>Recent efforts have explored learning-based methods, such as neural CBFs, to address this issue.<n>We propose a novel framework that leverages split-conformal prediction to generate formally verified neural CBFs.
• Score: 2.092779643426281
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Control Barrier Functions (CBFs) are a practical approach for designing safety-critical controllers, but constructing them for arbitrary nonlinear dynamical systems remains a challenge. Recent efforts have explored learning-based methods, such as neural CBFs (NCBFs), to address this issue. However, ensuring the validity of NCBFs is difficult due to potential learning errors. In this letter, we propose a novel framework that leverages split-conformal prediction to generate formally verified neural CBFs with probabilistic guarantees based on a user-defined error rate, referred to as CP-NCBF. Unlike existing methods that impose Lipschitz constraints on neural CBF-leading to scalability limitations and overly conservative safe sets--our approach is sample-efficient, scalable, and results in less restrictive safety regions. We validate our framework through case studies on obstacle avoidance in autonomous driving and geo-fencing of aerial vehicles, demonstrating its ability to generate larger and less conservative safe sets compared to conventional techniques.

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