Neural control barrier functions enable safer robot navigation
VertexCBF: Improving Neural Control Barrier Functions via Vertex-Restricted Control Search
RoboticsMachine Learning
Summary
Keeping robots safe is important as they become more common. Control barrier functions (CBFs) help robots avoid danger, but existing methods can be too cautious or hard to use. The authors created VertexCBF, a new approach that uses neural networks to better learn safe behaviors efficiently and understandably. Their method works on different robots, making safe zones larger and avoiding failures seen in older methods. They tested it on a real robot that safely avoided people walking nearby.
What this means in practice
- •For robotics engineers: Design safer autonomous robots that navigate complex environments without excessive conservatism or failure.
- •For mobile robot developers: Enhance navigation algorithms for mobile robots to safely avoid pedestrians using neural control barrier functions.
Authors
Bojan Derajić, Sebastian Bernhard, Wolfgang Hönig
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.