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

LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

LLM A: Human in the Loop Large Language Models Enabled A Search for Robotics