IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912939285413888 |
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| author | Su, Xiangyu Xu, Juzhan van Kaick, Oliver Xu, Kai Hu, Ruizhen |
| author_facet | Su, Xiangyu Xu, Juzhan van Kaick, Oliver Xu, Kai Hu, Ruizhen |
| contents | In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_02669 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models Su, Xiangyu Xu, Juzhan van Kaick, Oliver Xu, Kai Hu, Ruizhen Robotics In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities, have shown potential in coordinating robots for simple household and manipulation tasks. However, in industrial scenarios, stricter sequential constraints and more complex dependencies within tasks present new challenges for LLMs. To address this, we propose IMR-LLM, a novel LLM-driven Industrial Multi-Robot task planning and program generation framework. Specifically, we utilize LLMs to assist in constructing disjunctive graphs and employ deterministic solving methods to obtain a feasible and efficient high-level task plan. Based on this, we use a process tree to guide LLMs to generate executable low-level programs. Additionally, we create IMR-Bench, a challenging benchmark that encompasses multi-robot industrial tasks across three levels of complexity. Experimental results indicate that our method significantly surpasses existing methods across all evaluation metrics. |
| title | IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.02669 |