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.
@article{arxiv.2506.15082,
title = {Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks},
author = {Yimian Ding and Jingzehua Xu and Guanwen Xie and Shuai Zhang and Yi Li},
journal= {arXiv preprint arXiv:2506.15082},
year = {2025}
}
Comments
This paper has been accepted by IROS 2025. Yimian Ding and Jingzehua Xu contributed equally to this work, and Jingzehua Xu is also the corresponding author of this paper