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arXiv · 2603.17022

Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability

Kasidit Muenprasitivej·Derya Aksaray
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Abstract

Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for dynamical systems, but solving high-dimensional HJ partial differential equations limits its use in real-time planning. This paper presents a contingency-aware multi-goal navigation framework that integrates learning-based reachability with sampling-based planning in unknown environments. We use Fourier Neural Operator (FNO) to approximate the solution operator of the Hamilton-Jacobi-Isaacs variational inequality under varying obstacle configurations. We first provide a theoretical under-approximation guarantee on the safe backward reach-avoid set, which enables formal safety certification of the learned reachable sets. Then, we integrate the certified reachable sets with an incremental multi-goal planner, which enforces reachable-set constraints and a recovery policy that guarantees finite-time return to a safe region. Overall, we demonstrate that the proposed framework achieves asymptotically optimal navigation with provable contingency behavior, and validate its performance through real-time deployment on KUKA's youBot in Webots simulation.

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BibTeXRIS

Kasidit Muenprasitivej, Derya Aksaray. 2026-03-17. Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability. https://arxiv.org/abs/2603.17022

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