End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
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Computer Science > Machine Learning
Title:End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
Abstract:We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs). Incorporating CBFs into end-to-end policy training requires embedding a quadratic-program-based safety filter as an optimization layer, but computational and differentiation bottlenecks have largely restricted prior approaches to low-dimensional systems, typically with at most 16 state dimensions. We address this limitation by combining operator splitting with the recently developed Jacobian-Free Backpropagation (JFB) method to enable scalable end-to-end training while preserving hard safety guarantees through the CBF safety filter. We justify this training methodology theoretically using nonsmooth analysis techniques and demonstrate its effectiveness on high-dimensional multi-agent nonlinear control problems with state and control dimensions up to 1200 and 400, respectively.
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY); Optimization and Control (math.OC) |
| Cite as: | arXiv:2607.20674 [cs.LG] |
| (or arXiv:2607.20674v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.20674
arXiv-issued DOI via DataCite (pending registration)
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