This research focuses on how Large Language Models (LLMs) can help with path planning for mobile embodied agents such as robots, in a human-in-the-loop and interactive manner. A novel framework named LLM A*, aims to leverage the commonsense of LLMs, and the utility-optimal A* is proposed to facilitate few-shot near-optimal path planning. Prompts are used to 1) provide LLMs with essential information like environment, cost, heuristics, etc.; 2) communicate human feedback to LLMs on intermediate planning results. This makes the whole path planning process a `white box' and human feedback guides LLM A* to converge quickly compared to other data-driven methods such as reinforcement learning-based (RL) path planning. In addition, it makes code-free path planning practical, henceforth promoting the inclusiveness of artificial intelligence techniques. Comparative analysis against A* and RL shows that LLM A* is more efficient in terms of search space and achieves an on-a-par path with A* and a better path than RL. The interactive nature of LLM A* also makes it a promising tool for deployment in collaborative human-robot tasks.

研究聚焦于大型语言模型如何以人机交互的方式帮助移动体代理（如机器人）的路径规划。提出了名为LLM A*的创新框架，旨在利用LLM的常识，采用效用最优的A*算法来实现少样本近最优路径规划。通过引导LLM以求解`白盒'的路径规划过程，并将人类反馈作为引导，相较于基于强化学习的规划方法，如RL，使得无需编码的路径规划变得实用，并且推广了人工智能技术的包容性。与A*和RL的对比分析表明，LLM A*在搜索空间方面更高效，能达到与A*相当的路径并优于RL。LLM A*的交互性还使其成为协作人机任务部署中具有潜力的工具。

LLM A*：人在循环大型语言模型启用的A*搜索用于机器人技术

LLM A：人在循环大型语言模型启用的A搜索用于机器人技术