Smart Multi-Agent Robot Task Planning using Large Language Models

This is a supplementary video for the paper, titled "SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models", by Shyam Sundar Kannan, Vishnunandan L. N. Venkatesh, and Byung-Cheol Min. It will be presented at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Abu Dhabi, UAE, October 13-17, 2024.

Paper (ArXiv) Link:https://arxiv.org/abs/2309.10062

Abstract: In this work, we introduce SMART-LLM, an innovative framework designed for embodied multi-robot task planning. SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models (LLMs), harnesses the power of LLMs to convert high-level task instructions provided as input into a multi-robot task plan. It accomplishes this by executing a series of stages, including task decomposition, coalition formation, and task allocation, all guided by programmatic LLM prompts within the few-shot prompting paradigm. We create a benchmark dataset designed for validating the multi-robot task planning problem, encompassing four distinct categories of high-level instructions that vary in task complexity. Our evaluation experiments span both simulation and real-world scenarios, demonstrating that the proposed model can achieve promising results for generating multi-robot task plans. The experimental videos, code, and datasets from the work can be found at https://sites.google.com/view/smart-llm.