mlsanityData to 14 Oct 2026

Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks

arXiv 2506.15082 ↗First posted 18 Jun 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This study presents a novel environment-aware reinforcement learning (RL) framework designed to augment the operational capabilities of autonomous underwater vehicles (AUVs) in underwater environments. Departing from traditional RL architectures, the proposed framework integrates an environment-aware network module that dynamically captures flow field data, effectively embedding this critical environmental information into the state space. This integration facilitates real-time environmental adaptation, significantly enhancing the AUV's situational awareness and decision-making capabilities. Furthermore, the framework incorporates AUV structure characteristics into the optimization process, employing a large language model (LLM)-based iterative refinement mechanism that leverages both environmental conditions and training outcomes to optimize task performance. Comprehensive experimental evaluations demonstrate the framework's superior performance, robustness and adaptability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 27 references that are in the map. First posted on arXiv 18 Jun 2025.

Builds on

8 of 27 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles30 Sep

Other references · 7

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
  3. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  4. 2021Learning High-Speed Flight in the Wildcited by 128
  5. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  6. 2023Learning Vision-Based Bipedal Locomotion for Challenging Terraincited by 42
  7. 2023Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learningcited by 3

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  1. 2025Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks
  2. 2025When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks
  3. 2024Enhancing Information Freshness: An AoI Optimized Markov Decision Process
  4. 2025Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions
  5. 2025Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle
  6. 2025EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control ↗
  7. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
  8. 2026Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation