  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/.
