  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}
