VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search

2026-09-11

AI Summary

本文提出VertexCBF框架，通过神经网络学习控制屏障函数，利用顶点受限的控制搜索解决现有方法保守性高、可扩展性和可解释性差的问题。

Abstract

As the number of autonomous robots continues to grow, safety becomes increasingly important. Control barrier functions (CBFs) provide a theoretically grounded framework for ensuring safety, but existing design methods often face limitations in effectiveness, scalability, or interpretability, and may result in overly conservative safe sets. In this paper, we propose \emph{VertexCBF}, a framework for learning neural CBFs in a scalable, systematic, and explainable way. We approximate the stationary Hamilton--Jacobi value function using a neural network trained via a combination of physics-informed and sparsely supervised learning. By exploiting control-affine dynamics and a convex polytope control set, under which the Hamiltonian is maximized at the control vertices, we efficiently generate supervision points via GPU-parallel vertex-restricted tree search, while a residual architecture guarantees that the learned CBF is never larger than the specified constraint function. We evaluate the method on 15 systems and compare it against relevant baselines, showing that it reliably recovers large safe sets where the baselines are conservative or fail completely. In addition, we perform a hardware experiment in which a mobile robot safely avoids pedestrians using a neural CBF trained with our method.

Problem

• autonomous robots
• safety
• control barrier functions
• effectiveness
• scalability

Innovation

• Neural Control Barrier Functions
• Physics-informed Learning
• Sparse Supervision
• Control-Affine Dynamics
• Convex Polytope