A Layered View of LLM Support in Underwater Robotics

原文：A Layered View of LLM Support in Underwater Robotics

作者：Federico Celi, Leonardo Zacchini, Gabriele Ferri

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2611.06162v1 (cs)

[Submitted on 4 Nov 2026]

Title:A Layered View of LLM Support in Underwater Robotics

Authors:Federico Celi, Leonardo Zacchini, Gabriele Ferri

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Abstract:This extended abstract discusses a specific and, in our experience, practically relevant role for large language models (LLMs) in underwater robotics. Our main premise is that, in underwater fielding missions, the immediate difficulty is often not the synthesis of new low-level control laws, but the operation of complex robotic systems under limited communications, partial observability, and expensive field time. In particular, a significant fraction of the effort during sea trials is devoted to fault diagnosis, telemetry interpretation, mission adjustment, and coordination across heterogeneous assets and operators. We argue that LLMs may be useful at this higher operational level, especially when grounded in structured vehicle state, alarms, logs, mission context, and existing planning or validation tools. At the same time, current limitations in grounding, consistency, and causal reasoning make these systems lack the predictability required to guarantee direct control, especially in real-world critical applications, although we do not exclude a future evolution in this direction. This viewpoint suggests a layered architecture in which conventional autonomy remains responsible for control, perception, estimation, and validated mission execution, whereas LLM-based modules serve as an additional support layer for debugging, operator interaction, constrained replanning support, orchestration, and post-mission analysis.

Comments:

Presented at the I-RIM 3D 2026 - Workshop n.30 How would you like intelligence in your robots? Interdisciplinary perspectives on intelligent machines

Subjects:

Robotics (cs.RO); Systems and Control (eess.SY)

Cite as:

arXiv:2611.06162 [cs.RO]

(or

arXiv:2611.06162v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2611.06162

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arXiv-issued DOI via DataCite (pending registration)

主题

机器人 · eess.SY

由「前沿雷达」于 2026-11-04 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。