{"ID":2880088,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14387","arxiv_id":"2508.14387","title":"DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models","abstract":"Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-end solutions in known environments, lacking both dynamic adaptation capabilities for online operation and explainability in the processes of planning. To address these issues, a novel framework (DEXTER-LLM) for dynamic task planning in unknown environments, integrates four modules: (i) a mission comprehension module that resolves partial ordering of tasks specified by natural languages or linear temporal logic formulas (LTL); (ii) an online subtask generator based on LLMs that improves the accuracy and explainability of task decomposition via multi-stage reasoning; (iii) an optimal subtask assigner and scheduler that allocates subtasks to robots via search-based optimization; and (iv) a dynamic adaptation and human-in-the-loop verification module that implements multi-rate, event-based updates for both subtasks and their assignments, to cope with new features and tasks detected online. The framework effectively combines LLMs' open-world reasoning capabilities with the optimality of model-based assignment methods, simultaneously addressing the critical issue of online adaptability and explainability. Experimental evaluations demonstrate exceptional performances, with 100% success rates across all scenarios, 160 tasks and 480 subtasks completed on average (3 times the baselines), 62% less queries to LLMs during adaptation, and superior plan quality (2 times higher) for compound tasks. Project page at https://tcxm.github.io/DEXTER-LLM/","short_abstract":"Online coordination of multi-robot systems in open and unknown environments faces significant challenges, particularly when semantic features detected during operation dynamically trigger new tasks. Recent large language model (LLMs)-based approaches for scene reasoning and planning primarily focus on one-shot, end-to-...","url_abs":"https://arxiv.org/abs/2508.14387","url_pdf":"https://arxiv.org/pdf/2508.14387v1","authors":"[\"Yuxiao Zhu\",\"Junfeng Chen\",\"Xintong Zhang\",\"Meng Guo\",\"Zhongkui Li\"]","published":"2025-08-20T03:27:23Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}