In the trending research of fusing Large Language Models (LLMs) and robotics, we aim to pave the way for innovative development of AI systems that can enable Autonomous Underwater Vehicles (AUVs) to seamlessly interact with humans in an intuitive manner. We propose OceanChat, a system that leverages a closed-loop LLM-guided task and motion planning framework to tackle AUV missions in the wild. LLMs translate an abstract human command into a high-level goal, while a task planner further grounds the goal into a task sequence with logical constraints. To assist the AUV with understanding the task sequence, we utilize a motion planner to incorporate real-time Lagrangian data streams received by the AUV, thus mapping the task sequence into an executable motion plan. Considering the highly dynamic and partially known nature of the underwater environment, an event-triggered replanning scheme is developed to enhance the system's robustness towards uncertainty. We also build a simulation platform HoloEco that generates photo-realistic simulation for a wide range of AUV applications. Experimental evaluation verifies that the proposed system can achieve improved performance in terms of both success rate and computation time. Project website: \url{https://sites.google.com/view/oceanchat}

翻译：在当前大语言模型（LLMs）与机器人技术融合的研究趋势中，我们旨在为人工智能系统的创新开发铺平道路，使自主水下航行器（AUVs）能够以直观方式与人类无缝交互。我们提出OceanChat系统，该系统利用闭环大语言模型引导的任务与运动规划框架，解决真实环境中AUV的任务执行问题。大语言模型将抽象的人类指令转化为高层目标，而任务规划器进一步将目标具象化为包含逻辑约束的任务序列。为协助AUV理解任务序列，我们采用运动规划器整合AUV接收的实时拉格朗日数据流，从而将任务序列映射为可执行的运动规划。鉴于水下环境高度动态且部分已知的特点，我们开发了事件触发重规划机制以增强系统应对不确定性的鲁棒性。此外，我们构建了仿真平台HoloEco，可为各类AUV应用生成逼真的仿真场景。实验评估表明，所提系统在成功率和计算时间两方面均能实现更优性能。项目网站：\url{https://sites.google.com/view/oceanchat}