EasyUUV: An LLM-Enhanced Universal and Lightweight Sim-to-Real Reinforcement Learning Framework for UUV Attitude Control

TL;DR

EasyUUV introduces an LLM-enhanced, lightweight RL framework that improves UUV attitude control robustness and adaptability in real-world underwater conditions, combining simulation training with runtime parameter tuning.

Contribution

The paper presents a novel LLM-integrated simulation-to-reality RL framework for UUV attitude control, enabling adaptive, robust performance without extensive real-world training.

Findings

Effective in diverse underwater conditions

Robust and adaptive attitude control demonstrated

Reproducible with open-source code

Abstract

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