mlsanityData to 14 Oct 2026

Ocean Diviner: A Diffusion-Augmented Reinforcement Learning Framework for AUV Robust Control in Underwater Tasks

arXiv 2507.11283 ↗First posted 15 Jul 2025cs.RO cs.SY eess.SYcited by 0

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

Abstract (arXiv)

Autonomous Underwater Vehicles (AUVs) are essential for marine exploration, yet their control remains highly challenging due to nonlinear dynamics and uncertain environmental disturbances. This paper presents a diffusion-augmented Reinforcement Learning (RL) framework for robust AUV control, aiming to improve AUV's adaptability in dynamic underwater environments. The proposed framework integrates two core innovations: (1) A diffusion-based action generation framework that produces physically feasible and high-quality actions, enhanced by a high-dimensional state encoding mechanism combining current observations with historical states and actions through a novel diffusion U-Net architecture, significantly improving long-horizon planning capacity for robust control. (2) A sample-efficient hybrid learning architecture that synergizes diffusion-guided exploration with RL policy optimization, where the diffusion model generates diverse candidate actions and the RL critic selects the optimal action, achieving higher exploration efficiency and policy stability in dynamic underwater environments. Extensive simulation experiments validate the framework's superior robustness and flexibility, outperforming conventional control methods in challenging marine conditions, offering enhanced adaptability and reliability for AUV operations in underwater tasks. Finally, we will release the code publicly soon to support future research in this area.

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 11 of its 30 references that are in the map. First posted on arXiv 15 Jul 2025.

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11 of 30 references.

References · 11

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  5. 2022Diffusion Models in Vision: A Surveycited by 1,015
  6. 2022Is Conditional Generative Modeling all you need for Decision-Making?cited by 516
  7. 2023AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Plannerscited by 149
  8. 2019Safe Reinforcement Learning Using Robust MPCcited by 85
  9. 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RLcited by 68
  10. 2024Enhancing Efficiency of Safe Reinforcement Learning via Sample Manipulationcited by 9
  11. 2025Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditionscited by 4

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  1. 2025When Motion Learns to Listen: Diffusion-Prior Lyapunov Actor-Critic Framework with LLM Guidance for Stable and Robust AUV Control in Underwater Tasks
  2. 2025Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
  3. 2026CORL-AUV: CFD Oriented Reinforcement Learning for Autonomous Underwater Vehicles
  4. 2024Learning to Swim: Reinforcement Learning for 6-DOF Control of Thruster-driven Autonomous Underwater Vehicles
  5. 2026Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach
  6. 2025Fast Policy Learning for 6-DOF Position Control of Underwater Vehicles
  7. 2026Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks
  8. 2026Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning