{"ID":2870290,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.12838","arxiv_id":"2509.12838","title":"Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models","abstract":"It is crucial to efficiently execute instructions such as \"Find an apple and a banana\" or \"Get ready for a field trip,\" which require searching for multiple objects or understanding context-dependent commands. This study addresses the challenging problem of determining which robot should be assigned to which part of a task when each robot possesses different situational on-site knowledge-specifically, spatial concepts learned from the area designated to it by the user. We propose a task planning framework that leverages large language models (LLMs) and spatial concepts to decompose natural language instructions into subtasks and allocate them to multiple robots. We designed a novel few-shot prompting strategy that enables LLMs to infer required objects from ambiguous commands and decompose them into appropriate subtasks. In our experiments, the proposed method achieved 47/50 successful assignments, outperforming random (28/50) and commonsense-based assignment (26/50). Furthermore, we conducted qualitative evaluations using two actual mobile manipulators. The results demonstrated that our framework could handle instructions, including those involving ad hoc categories such as \"Get ready for a field trip,\" by successfully performing task decomposition, assignment, sequential planning, and execution.","short_abstract":"It is crucial to efficiently execute instructions such as \"Find an apple and a banana\" or \"Get ready for a field trip,\" which require searching for multiple objects or understanding context-dependent commands. This study addresses the challenging problem of determining which robot should be assigned to which part of a...","url_abs":"https://arxiv.org/abs/2509.12838","url_pdf":"https://arxiv.org/pdf/2509.12838v2","authors":"[\"Kento Murata\",\"Shoichi Hasegawa\",\"Tomochika Ishikawa\",\"Yoshinobu Hagiwara\",\"Akira Taniguchi\",\"Lotfi El Hafi\",\"Tadahiro Taniguchi\"]","published":"2025-09-16T09:00:25Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}