SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866929286304235520 |
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| author | Kannan, Shyam Sundar Venkatesh, Vishnunandan L. N. Min, Byung-Cheol |
| author_facet | Kannan, Shyam Sundar Venkatesh, Vishnunandan L. N. Min, Byung-Cheol |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_10062 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models Kannan, Shyam Sundar Venkatesh, Vishnunandan L. N. Min, Byung-Cheol Robotics 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/. |
| title | SMART-LLM: Smart Multi-Agent Robot Task Planning using Large Language Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2309.10062 |