End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers

• URL: http://arxiv.org/abs/2607.20674v1
• Date: Wed, 22 Jul 2026 19:17:11 GMT
• Title: End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layers
• Abstract summary: We consider the problem of learning high-dimensional semi-global feedback controllers under hard safety constraints enforced by control barrier functions (CBFs)<n> incorporating CBFs into end-to-end policy training requires embedding a quadratic-program-based safety filter as an optimization layer.<n>We address this limitation by combining operator splitting with the recently developed Jacobian-Free Backpropagation (JFB) method to enable scalable end-to-end training.
• Score: 2.9195558758083635
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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