Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle

• URL: http://arxiv.org/abs/2604.13325v1
• Date: Tue, 14 Apr 2026 22:13:25 GMT
• Title: Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
• Abstract summary: Safety filters provide a practical approach for enforcing safety constraints in autonomous systems.<n>While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation.<n>In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Principle.
• Score: 5.856990501507199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety filters provide a practical approach for enforcing safety constraints in autonomous systems. While learning-based tools scale to high-dimensional systems, their performance depends on informative data that includes states likely to lead to constraint violation, which can be difficult to efficiently sample in complex, high-dimensional systems. In this work, we characterize trajectories that barely avoid safety violations using the Pontryagin Maximum Principle. These boundary trajectories are used to guide data collection for learned Hamilton-Jacobi Reachability, concentrating learning efforts near safety-critical states to improve efficiency. The learned Control Barrier Value Function is then used directly for safety filtering. Simulations and experimental validation on a shared-control automotive racing application demonstrate PMP sampling improves learning efficiency, yielding faster convergence, reduced failure rates, and improved safe set reconstruction, with wall times around 3ms.

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