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20242026
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cs.RO2026

Foundation models on the bridge: Semantic hazard detection and safety maneuvers for maritime autonomy with vision-language models

Kim Alexander Christensen, Andreas Gudahl Tufte, Alexey Gusev +5

The draft IMO MASS Code requires autonomous and remotely supervised maritime vessels to detect departures from their operational design domain, enter a predefined fallback that not…

cs.RO2025

Preventing Robotic Jailbreaking via Multimodal Domain Adaptation

Francesco Marchiori, Rohan Sinha, Christopher Agia +4

Large Language Models (LLMs) and Vision-Language Models (VLMs) are increasingly deployed in robotic environments but remain vulnerable to jailbreaking attacks that bypass safety me…

cs.RO2025

Real-Time Out-of-Distribution Failure Prevention via Multi-Modal Reasoning

Milan Ganai, Rohan Sinha, Christopher Agia +3

While foundation models offer promise toward improving robot safety in out-of-distribution (OOD) scenarios, how to effectively harness their generalist knowledge for real-time, dyn…

cs.RO2025

CUPID: Curating Data your Robot Loves with Influence Functions

Christopher Agia, Rohan Sinha, Jingyun Yang +5

In robot imitation learning, policy performance is tightly coupled with the quality and composition of the demonstration data. Yet, developing a precise understanding of how indivi…

cs.RO2025

RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models

Jacky Kwok, Christopher Agia, Rohan Sinha +5

Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in visuomotor control, yet ensuring their robustness in unstructured real-world environments remains a…

cs.RO2024

Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress

Christopher Agia, Rohan Sinha, Jingyun Yang +4

Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies a…