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, universal, 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 outstanding performance of EasyUUV in achieving robust and adaptive UUV attitude control across diverse underwater conditions. To facilitate reproducibility and further research, the source code, LLM prompts, and supplementary video are provided in the following repositories: Homepage: https://360zmem.github.io/easyuuv/ Video:https://youtu.be/m2yLQzxiIL

翻译：尽管无人水下航行器（UUV）姿态控制领域近期取得了进展，现有方法在泛化性、对现实世界扰动的鲁棒性以及高效部署方面仍面临挑战。为应对上述问题，本文提出了EasyUUV，一个用于UUV鲁棒姿态控制的大语言模型（LLM）增强、通用且轻量级的仿真到现实强化学习（RL）框架。EasyUUV将并行化RL训练与混合控制架构相结合，其中学习策略输出高级姿态修正指令，由自适应S-Surface控制器执行。框架进一步集成了多模态LLM，利用视觉与文本反馈在运行时自适应调整控制器参数，从而实现对未建模动态的无训练适应。此外，我们开发了一种低成本六自由度UUV平台，并应用了通过高效并行仿真训练的RL策略。大量仿真与真实世界实验验证了EasyUUV在不同水下环境中实现鲁棒自适应UUV姿态控制的有效性与卓越性能。为促进可复现性与进一步研究，源代码、LLM提示词及补充视频已发布于以下存储库：主页：https://360zmem.github.io/easyuuv/ 视频：https://youtu.be/m2yLQzxiIL