VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search
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 c...
| Authors | Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig |
|---|---|
| Primary category | cs.RO |
| Published | 11 September 2026 |
| Last updated at source | 23 September 2026 |
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