Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915644345155584 |
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| author | Ding, Yimian Xu, Jingzehua Xie, Guanwen Zhang, Shuai Li, Yi |
| author_facet | Ding, Yimian Xu, Jingzehua Xie, Guanwen Zhang, Shuai Li, Yi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15082 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks Ding, Yimian Xu, Jingzehua Xie, Guanwen Zhang, Shuai Li, Yi Systems and Control 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. |
| title | Make Your AUV Adaptive: An Environment-Aware Reinforcement Learning Framework For Underwater Tasks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2506.15082 |