用大模型让水下机器人在仿真中训练后，能自适应真实环境中的姿态控制。
EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control
- 结合强化学习与自适应S面控制器，实现高鲁棒性姿态调节。
- 通过多模态大模型实时调整参数，无需重新训练即可应对未知干扰。
- 轻量级框架支持低成本6自由度水下平台部署，适合工程应用。
尽管无人水下航行器（UUV）姿态控制取得进展，现有方法仍面临泛化能力弱、对真实世界扰动鲁棒性差及部署效率低的问题。本文提出EasyUUV，一个基于大语言模型（LLM）增强的、平台无关且轻量级的仿真到现实强化学习（RL）框架，用于鲁棒的UUV姿态控制。该框架采用并行化强化学习训练与混合控制架构，由学习到的策略输出高层姿态修正，交由自适应S面控制器执行。进一步集成多模态LLM，利用视觉与文本反馈在运行时自适应调整控制器参数，实现对未建模动态的零训练适应。我们还构建了低成本6-DoF UUV平台，并使用高效并行化仿真训练的策略进行验证。大量仿真与真实实验表明，EasyUUV在多种水下环境下均能实现稳健且自适应的姿态控制。为促进可复现性，源代码、LLM提示词、视频及补充材料已公开于以下链接：主页：https://360zmem.github.io/easyuuv/ 视频：https://youtu.be/m2yLQzxiIL 补充材料：https://drive.google.com/file/d/1ImMiyGIPoPyj2ATnXQwbiuOroTSZ2SSs
原文摘要 · Abstract (English)
Despite recent advances in Unmanned Underwater Vehicle (UUV) attitude control, existing methods still struggle with generalizability, robustness to real-world disturbances, and efficient deployment. To address the above challenges, this paper presents EasyUUV, a Large Language Model (LLM)-enhanced, platform-agnostic, and lightweight simulation-to-reality reinforcement learning (RL) framework for robust attitude control of UUVs. EasyUUV combines parallelized RL training with a hybrid control architecture, where a learned policy outputs high-level attitude corrections executed by an adaptive S-Surface controller. A multimodal LLM is further integrated to adaptively tune controller parameters at runtime using visual and textual feedback, enabling training-free adaptation to unmodeled dynamics. Also, we have developed a low-cost 6-DoF UUV platform and applied an RL policy trained through efficient parallelized simulation. Extensive simulation and real-world experiments validate the effectiveness and adaptive performance of EasyUUV in achieving robust and adaptive UUV attitude control across diverse underwater conditions. To facilitate reproducibility, the source code, LLM prompts, video, and the supplementary material are provided in the following repositories: Homepage: https://360zmem.github.io/easyuuv/ Video:https://youtu.be/m2yLQzxiIL Supplementary Material: https://drive.google.com/file/d/1ImMiyGIPoPyj2ATnXQwbiuOroTSZ2SSs
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