{"ID":2888475,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23735","arxiv_id":"2507.23735","title":"Distributed AI Agents for Cognitive Underwater Robot Autonomy","abstract":"Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework to enable advanced cognitive capabilities in Autonomous Underwater Vehicles. UROSA decentralises cognition into specialised AI agents responsible for multimodal perception, adaptive reasoning, dynamic mission planning, and real-time decision-making. Central innovations include flexible agents dynamically adapting their roles, retrieval-augmented generation utilising vector databases for efficient knowledge management, reinforcement learning-driven behavioural optimisation, and autonomous on-the-fly ROS 2 node generation for runtime functional extensibility. Extensive empirical validation demonstrates UROSA's promising adaptability and reliability through realistic underwater missions in simulation and real-world deployments, showing significant advantages over traditional rule-based architectures in handling unforeseen scenarios, environmental uncertainties, and novel mission objectives. This work not only advances underwater autonomy but also establishes a scalable, safe, and versatile cognitive robotics framework capable of generalising to a diverse array of real-world applications.","short_abstract":"Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot...","url_abs":"https://arxiv.org/abs/2507.23735","url_pdf":"https://arxiv.org/pdf/2507.23735v2","authors":"[\"Markus Buchholz\",\"Ignacio Carlucho\",\"Michele Grimaldi\",\"Yvan R. Petillot\"]","published":"2025-07-31T17:18:55Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.MA\"]","methods":"[\"RAG\",\"Reinforcement Learning\",\"Language Model\",\"Generative Adversarial Network\"]","has_code":false}