用大模型模拟人类协作，让不同机器人高效配合完成复杂任务
Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models
- 每个机器人配一个大模型，动态分组协商任务
- 复杂任务效率提升40%以上，简单任务成功率不降
- 适合需要长期协作的异构机器人系统研究者
多机器人协作常需在空间受限和环境不确定条件下，由异构机器人长期协同完成任务。尽管大语言模型（LLMs）在推理与规划上表现优异，其在协调控制方面的潜力尚未充分挖掘。受人类团队协作启发，我们提出CLiMRS（基于大语言模型的异构多机器人协作系统），一种大模型间的自适应群体协商框架。该框架为每台机器人配备一个大模型代理，并通过通用提案规划器动态组建子群体。在每个子群体内，由子群管理者主导感知驱动的多大模型讨论，生成行动指令。执行结果与环境变化共同提供反馈。这一‘分组-规划-执行-反馈’循环实现了高效规划与鲁棒执行。为评估该能力，我们引入CLiMBench——一个包含挑战性装配任务的异构多机器人基准测试集。实验表明，CLiMRS优于最佳基线，在复杂任务上效率提升超过40%，同时保持对简单任务的成功率。结果证明，借鉴人类群体形成与协商原则可显著提升异构多机器人协作效率。代码已开源：https://github.com/song-siqi/CLiMRS。
原文摘要 · Abstract (English)
Multi-robot collaboration tasks often require heterogeneous robots to work together over long horizons under spatial constraints and environmental uncertainties. Although Large Language Models (LLMs) excel at reasoning and planning, their potential for coordinated control has not been fully explored. Inspired by human teamwork, we present CLiMRS (Cooperative Large-Language-Model-Driven Heterogeneous Multi-Robot System), an adaptive group negotiation framework among LLMs for multi-robot collaboration. This framework pairs each robot with an LLM agent and dynamically forms subgroups through a general proposal planner. Within each subgroup, a subgroup manager leads perception-driven multi-LLM discussions to get commands for actions. Feedback is provided by both robot execution outcomes and environment changes. This grouping-planning-execution-feedback loop enables efficient planning and robust execution. To evaluate these capabilities, we introduce CLiMBench, a heterogeneous multi-robot benchmark of challenging assembly tasks. Our experiments show that CLiMRS surpasses the best baseline, achieving over 40% higher efficiency on complex tasks without sacrificing success on simpler ones. Overall, our results demonstrate that leveraging human-inspired group formation and negotiation principles significantly enhances the efficiency of heterogeneous multi-robot collaboration. Our code is available here: https://github.com/song-siqi/CLiMRS.
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