Distributed AI Agents for Cognitive Underwater Robot Autonomy

• URL: http://arxiv.org/abs/2507.23735v2
• Date: Mon, 04 Aug 2025 08:56:21 GMT
• Title: Distributed AI Agents for Cognitive Underwater Robot Autonomy
• Authors: Markus Buchholz, Ignacio Carlucho, Michele Grimaldi, Yvan R. Petillot,
• Abstract summary: This paper presents Underwater Robot Self-Organizing Autonomy (UROSA)<n>UROSA is a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework.<n>Central innovations include flexible agents dynamically adapting their roles, retrieval-augmented generation, and autonomous on-the-fly ROS 2 node generation.
• Score: 5.644612398323221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions [70.72279728350763]
  Self-evolving agentic artificial intelligence (AI) offers a new paradigm for future wireless systems.<n>Unlike static AI models, self-evolving agents embed an autonomous evolution cycle that updates models, tools, and in response to environmental dynamics.<n>This paper presents a comprehensive overview of self-evolving agentic AI, highlighting its layered architecture, life cycle, and key techniques.
  arXiv  Detail & Related papers  (2025-10-07T05:45:25Z)
• A roadmap for AI in robotics [55.87087746398059]
  We are witnessing growing excitement in robotics at the prospect of leveraging the potential of AI to tackle some of the outstanding barriers to the full deployment of robots in our daily lives.<n>This article offers an assessment of what AI for robotics has achieved since the 1990s and proposes a short- and medium-term research roadmap listing challenges and promises.
  arXiv  Detail & Related papers  (2025-07-26T15:18:28Z)
• Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review [4.540236408836132]
  We present the first systematic review of the integration of foundation models in mobile service robotics.<n>We explore the role of such models in enabling real-time sensor fusion, language-conditioned control, and adaptive task execution.<n>We also discuss real-world applications in the domestic assistance, healthcare, and service automation sectors.
  arXiv  Detail & Related papers  (2025-05-26T20:08:09Z)
• RoboRAN: A Unified Robotics Framework for Reinforcement Learning-Based Autonomous Navigation [15.548637925166986]
  We present a multi-domain framework for training, evaluating and deploying RL-based navigation policies across diverse robotic platforms and operational environments.<n>Our work presents four key contributions: (1) a scalable and modular framework, facilitating seamless robot-task interchangeability and reproducible training pipelines; (2) sim-to-real transfer demonstrated through real-world experiments with multiple robots; and (3) the release of the first open-source API for deploying Isaac Lab-trained policies to real robots.
  arXiv  Detail & Related papers  (2025-05-20T15:48:23Z)
• Generative AI for Autonomous Driving: Frontiers and Opportunities [145.6465312554513]
  This survey delivers a comprehensive synthesis of the emerging role of GenAI across the autonomous driving stack.<n>We begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models.<n>We categorize practical applications, such as synthetic data generalization, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI.
  arXiv  Detail & Related papers  (2025-05-13T17:59:20Z)
• RoBridge: A Hierarchical Architecture Bridging Cognition and Execution for General Robotic Manipulation [90.81956345363355]
  RoBridge is a hierarchical intelligent architecture for general robotic manipulation.<n>It consists of a high-level cognitive planner (HCP) based on a large-scale pre-trained vision-language model (VLM)<n>It unleashes the procedural skill of reinforcement learning, effectively bridging the gap between cognition and execution.
  arXiv  Detail & Related papers  (2025-05-03T06:17:18Z)
• Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study [3.794859469462058]
  Human-Centered AI (HCAI) aims to balance human control and automation.<n>This paper presents a bibliometric analysis of intelligent autonomous robotic systems using SciMAT and VOSViewer.<n>The findings highlight academic trends, emerging topics, and AI's role in self-adaptive robotic behaviour.
  arXiv  Detail & Related papers  (2025-04-28T14:45:48Z)
• REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation [57.628771707989166]
  We propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution.<n>ReMAC incorporates two key modules: a self-reflection module performing pre-conditions and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning.
  arXiv  Detail & Related papers  (2025-03-28T03:51:40Z)
• Universal AI maximizes Variational Empowerment [0.0]
  We build on the existing framework of Self-AIXI -- a universal learning agent that predicts its own actions.<n>We argue that power-seeking tendencies of universal AI agents can be explained as an instrumental strategy to secure future reward.<n>Our main contribution is to show how these motivations systematically lead universal AI agents to seek and sustain high-optionality states.
  arXiv  Detail & Related papers  (2025-02-20T02:58:44Z)
• GRAPPA: Generalizing and Adapting Robot Policies via Online Agentic Guidance [15.774237279917594]
  We propose an agentic framework for robot self-guidance and self-improvement.<n>Our framework iteratively grounds a base robot policy to relevant objects in the environment.<n>We demonstrate that our approach can effectively guide manipulation policies to achieve significantly higher success rates.
  arXiv  Detail & Related papers  (2024-10-09T02:00:37Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
  This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
  arXiv  Detail & Related papers  (2023-05-20T17:20:12Z)
• Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems [79.07468367923619]
  We propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC) We design an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards. We show that our proposed ActPC agent performs well in the face of sparse (extrinsic) reward signals and is competitive with or outperforms several powerful backprop-based RL approaches.
  arXiv  Detail & Related papers  (2022-09-19T16:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.