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

• URL: http://arxiv.org/abs/2312.01797v2
• Date: Thu, 20 Jun 2024 18:50:52 GMT
• Title: LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics
• Authors: Hengjia Xiao, Peng Wang,
• Abstract summary: This research focuses on how Large Language Models (LLMs) can help with (path) planning for mobile embodied agents such as robots. 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. This approach takes human feedback on board and renders the entire planning process transparent (akin to a white box') to humans.
• Score: 3.567107449359775
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 for two main purposes: 1) to provide LLMs with essential information like environments, costs, heuristics, etc.; 2) to communicate human feedback on intermediate planning results to LLMs. This approach takes human feedback on board and renders the entire planning process transparent (akin to a `white box') to humans. Moreover, it facilitates code-free path planning, thereby fostering the accessibility and inclusiveness of artificial intelligence techniques to communities less proficient in coding. Comparative analysis against A* and RL demonstrates that LLM A* exhibits greater efficiency in terms of search space and achieves paths comparable to A* while outperforming RL. The interactive nature of LLM A* also makes it a promising tool for deployment in collaborative human-robot tasks. Codes and Supplemental Materials can be found at GitHub: https://github.com/speedhawk/LLM-A-.

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