让异构机器人团队用大模型实现动态协同，遇事自动重规划。
LLM-Based Generalizable Hierarchical Task Planning and Execution for Heterogeneous Robot Teams with Event-Driven Replanning
- 用大模型分层管理任务，结合规则与实时状态分配工作。
- 物理实验中90%以上任务成功恢复，人机协作成功率100%。
- 适合需要灵活应变的多机器人系统研发者参考。
本文提出CoMuRoS（协同多机器人系统），一种适用于异构机器人团队的可泛化分层架构，统一集中式决策与分布式执行，并支持事件驱动的重规划。任务管理大模型解析自然语言目标，结合静态规则与动态上下文（任务、历史、机器人及任务状态、事件）进行任务分类与子任务分配。每个机器人运行本地大模型，将基础技能（ROS2节点、策略）组合成可执行的Python代码；同时，基于视觉大模型（VLMs）和图像处理的本机感知持续监测事件，并判断其是否相关。任务失败或用户意图变化会触发重规划，使机器人能协助队友、恢复任务或请求人工帮助。硬件实验表明系统能自主应对干扰事件，过滤无关干扰，并实现紧密协作运输，展现出涌现的人机协同能力：多机器人协同物体恢复成功率9/10，协调运输成功率8/8，人助恢复成功率5/5。仿真研究验证了意图感知重规划。一个包含22个场景（每场景3个任务，约20个机器人）的文本基准评估了任务分配、分类、交并比（IoU）、可执行性与正确性，多个大模型平均得分较高（如正确性达0.91），独立重规划测试集更达到1.0正确率。相比以往基于大模型的系统，CoMuRoS首次在真实机器人上实现运行时、事件驱动的重规划，展现了鲁棒、灵活的多机器人及人机协作能力。
原文摘要 · Abstract (English)
This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifies centralized deliberation with decentralized execution, and supports event-driven replanning. A Task Manager LLM interprets natural-language goals, classifies tasks, and allocates subtasks using static rules plus dynamic contexts (task, history, robot and task status, and events).Each robot runs a local LLM that composes executable Python code from primitive skills (ROS2 nodes, policies), while onboard perception (VLMs/image processing) continuously monitors events and classifies them into relevant or irrelevant to the task. Task failures or user intent changes trigger replanning, allowing robots to assist teammates, resume tasks, or request human help. Hardware studies demonstrate autonomous recovery from disruptive events, filtering of irrelevant distractions, and tightly coordinated transport with emergent human-robot cooperation (e.g., multirobot collaborative object recovery success rate: 9/10, coordinated transport: 8/8, human-assisted recovery: 5/5).Simulation studies show intention-aware replanning. A curated textual benchmark spanning 22 scenarios (3 tasks each, around 20 robots) evaluates task allocation, classification, IoU, executability, and correctness, with high average scores (e.g., correctness up to 0.91) across multiple LLMs, a separate replanning set (5 scenarios) achieves 1.0 correctness. Compared with prior LLM-based systems, CoMuRoS uniquely demonstrates runtime, event-driven replanning on physical robots, delivering robust, flexible multi-robot and human-robot collaboration.
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