VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search
This paper approximate the stationary Hamilton--Jacobi value function using a neural network trained via a combination of physics-informed and sparsely supervised learning and proposes VertexCBF, a framework for learning neural CBFs in a scalable, systematic, and explainable way.
Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig
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