Neural Control Barrier Functions from Physics Informed Neural Networks

• URL: http://arxiv.org/abs/2504.11045v1
• Date: Tue, 15 Apr 2025 10:13:30 GMT
• Title: Neural Control Barrier Functions from Physics Informed Neural Networks
• Authors: Shreenabh Agrawal, Manan Tayal, Aditya Singh, Shishir Kolathaya,
• Abstract summary: This paper introduces a novel class of neural CBFs that leverages a physics-inspired neural network framework.<n>By utilizing reciprocal CBFs instead of zeroing CBFs, the proposed framework allows for the specification of flexible, user-defined safe regions.
• Score: 2.092779643426281
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As autonomous systems become increasingly prevalent in daily life, ensuring their safety is paramount. Control Barrier Functions (CBFs) have emerged as an effective tool for guaranteeing safety; however, manually designing them for specific applications remains a significant challenge. With the advent of deep learning techniques, recent research has explored synthesizing CBFs using neural networks-commonly referred to as neural CBFs. This paper introduces a novel class of neural CBFs that leverages a physics-inspired neural network framework by incorporating Zubov's Partial Differential Equation (PDE) within the context of safety. This approach provides a scalable methodology for synthesizing neural CBFs applicable to high-dimensional systems. Furthermore, by utilizing reciprocal CBFs instead of zeroing CBFs, the proposed framework allows for the specification of flexible, user-defined safe regions. To validate the effectiveness of the approach, we present case studies on three different systems: an inverted pendulum, autonomous ground navigation, and aerial navigation in obstacle-laden environments.

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