Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability
Contingency-Aware Planning via Certified Neural Hamilton-Jacobi Reachability
This research introduces a learning-based approach to Hamilton-Jacobi reachability that enables real-time contingency-aware planning in unknown environments. By using Fourier Neural Operators (FNO) to approximate the HJ solution operator and applying robust scenario optimization recast as multiple hypothesis testing, the method provides probabilistic safety guarantees and ensures system recovery to safe regions within finite time under disturbances. Numerical results demonstrate real-time updates of reach-avoid sets on a youBot simulation.
The paper proposes a method combining Fourier Neural Operators with certified hypothesis testing to achieve real-time, contingency-aware planning with formal safety guarantees in unknown environments.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ribed safety level. Within this certified set, the induced recovery policy is guaranteed to return the system to a safe region in finite time under any admissible disturbance. Numerical results show that the learning and certification pipeline updates reach-avoid sets at real-time rates and is demonstrated on youBot simulation.
Open source S4
arXiv:2603.17022v2 Announce Type: replace Abstract: Hamilton-Jacobi (HJ) reachability provides formal safety guarantees for dynamical systems, but repeatedly solving the HJ partial differential equation in changing environments limits its use in real-time planning. This paper introduces learning-based reachability and an online safety certification that together enable real-time contingency-aware planning in unknown
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org