Enumeration-Guided Training of Neural Hamilton-Jacobi Safety Certificates
Hamilton-Jacobi (HJ) reachability analysis offers formal safety guarantees for control systems, but training accurate neural certificates remains difficult, particularly when unsafe regions are hard to characterize. We propose a verification-guided training framework that addresses this by using probabilistic enumeration of safety constraints to detect forward-invariance violations, i.e., states where the learned certificate incorrectly classifies unsafe behavior. This produces a compact, targeted set of counterexamples that more precisely captures the failure boundary than inputs generated by standard adversarial attack methods. Crucially, using this representation to fine-tune a pretrai...
| Authors | Mihaela-Larisa Clement, Tosca Pierro, Ezio Bartocci et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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