SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models

2023/09/18 by Shyam Sundar Kannan, Kannan, Shyam Sundar, Vishnunandan L. N. Venkatesh +3 · 32 citations
Computer Science · #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Multimodal Machine Learning Applications #Robotics (cs.RO) #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2309.10062

openalex publication_date 2023/09/18 · openalex created_date 2023/09/21 · openalex updated_date 2026/07/28

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/.

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