用大模型让多机器人在未知环境动态协作，又快又透明。
DEXTER-LLM: Dynamic and Explainable Coordination of Multi-Robot Systems in Unknown Environments via Large Language Models
- 用大模型分步推理分解任务，提升准确性和可解释性。
- 实测完成160个任务、480个子任务，是基线3倍，成功率100%。
- 支持在线动态调整，适合需要实时响应的智能系统研发者。
开放未知环境中多机器人系统的在线协调面临挑战，尤其当运行中检测到语义特征并触发新任务时。现有基于大语言模型（LLM）的场景推理与规划方法多聚焦于已知环境的一次性端到端解决方案，缺乏在线动态适应能力与过程可解释性。为此，提出DEXTER-LLM框架，包含四个模块：(i) 任务理解模块，解析自然语言或线性时序逻辑（LTL）中的部分顺序任务；(ii) 基于LLM的在线子任务生成器，通过多阶段推理提升任务分解的准确性和可解释性；(iii) 基于搜索优化的最优子任务分配与调度器；(iv) 动态适应与人机协同验证模块，实现多速率、事件驱动的任务与分配更新，以应对在线新发现特征与任务。该框架结合大模型的开放世界推理能力与模型驱动分配的最优性，同时解决在线适应性与可解释性问题。实验表明，在所有场景中成功率均达100%，平均完成160项任务、480个子任务（为基线3倍），自适应阶段减少62%的LLM调用，复合任务计划质量提升2倍。
原文摘要 · Abstract (English)
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/
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