We develop a hierarchical LLM-task-motion planning and replanning framework to efficiently ground an abstracted human command into tangible Autonomous Underwater Vehicle (AUV) control through enhanced representations of the world. We also incorporate a holistic replanner to provide real-world feedback with all planners for robust AUV operation. While there has been extensive research in bridging the gap between LLMs and robotic missions, they are unable to guarantee success of AUV applications in the vast and unknown ocean environment. To tackle specific challenges in marine robotics, we design a hierarchical planner to compose executable motion plans, which achieves planning efficiency and solution quality by decomposing long-horizon missions into sub-tasks. At the same time, real-time data stream is obtained by a replanner to address environmental uncertainties during plan execution. Experiments validate that our proposed framework delivers successful AUV performance of long-duration missions through natural language piloting.

翻译：我们提出了一种分层的大语言模型-任务-运动规划与再规划框架，通过增强的世界表征将抽象的人类指令高效转化为具体的自主水下航行器控制。同时，我们引入了一个整体再规划器，为所有规划器提供实时反馈以确保AUV的鲁棒运行。尽管已有大量研究致力于弥合大语言模型与机器人任务之间的鸿沟，但这些方法无法保证在广阔未知海洋环境中AUV应用的成功。为应对海洋机器人的特定挑战，我们设计了一个分层规划器来生成可执行的运动规划，通过将长时间任务分解为子任务来实现规划效率与解质量的平衡。同时，再规划器获取实时数据流以应对规划执行过程中的环境不确定性。实验验证表明，所提出的框架能够通过自然语言操控实现AUV在长时间任务中的成功表现。