Staying on Spec: Real-Time Monitoring under Uncertainty with a Maritime Case Study
arXiv:2608.02811v1 Announce Type: new Abstract: Robotic systems must operate under uncertainty while satisfying complex task and safety specifications. Monitoring such specifications under uncertainty remains challenging, as existing formulations typically require extensive data or explicit uncertainty distributions. In this paper, we propose a real-time monitoring framework that reduces data requirements by leveraging data-driven reachable sets for specification evaluation. We instantiate the framework for maritime navigation, where complex specifications arise from traffic rules. We develop a data-efficient pipeline for constructing reachable sets and derive a monitoring formulation suitable for real-time deployment. Simulation and hardware experiments demonstrate robust monitoring under realistic disturbances, achieving improved risk detection compared to state-of-the-art metrics.
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[Submitted on 3 Aug 2026]
Title:Staying on Spec: Real-Time Monitoring under Uncertainty with a Maritime Case Study
View a PDF of the paper titled Staying on Spec: Real-Time Monitoring under Uncertainty with a Maritime Case Study, by Elizabeth Dietrich and 5 other authors
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Abstract:Robotic systems must operate under uncertainty while satisfying complex task and safety specifications. Monitoring such specifications under uncertainty remains challenging, as existing formulations typically require extensive data or explicit uncertainty distributions. In this paper, we propose a real-time monitoring framework that reduces data requirements by leveraging data-driven reachable sets for specification evaluation. We instantiate the framework for maritime navigation, where complex specifications arise from traffic rules. We develop a data-efficient pipeline for constructing reachable sets and derive a monitoring formulation suitable for real-time deployment. Simulation and hardware experiments demonstrate robust monitoring under realistic disturbances, achieving improved risk detection compared to state-of-the-art metrics.
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2608.02811 [cs.RO]
(or arXiv:2608.02811v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.02811
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Elizabeth Dietrich [view email] [v1] Mon, 3 Aug 2026 19:06:29 UTC (6,109 KB)
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