利用自适应群体谈判实现异构多机器人协作的大型语言模型
Leveraging Adaptive Group Negotiation for Heterogeneous Multi-Robot Collaboration with Large Language Models
AI总结：
CLiMRS通过自适应群体谈判框架提升异构多机器人协作效率，实验显示在复杂任务中效率提升超40%。
摘要：
多机器人协作任务往往需要异构机器人在空间约束和环境不确定性下长时间合作。尽管大型语言模型（LLMs）在推理和规划方面表现出色，但其在协同控制方面的潜力尚未得到充分探索。受人类团队合作的启发，我们提出了CLiMRS（协作大型语言模型驱动的异构多机器人系统），一种在多机器人协作中用于LLM的自适应群体谈判框架。该框架为每个机器人配对一个LLM代理，并通过通用提案计划器动态形成子组。在每个子组内，子组经理领导由感知驱动的多LLM讨论以获取行动指令。反馈来自机器人执行结果和环境变化。这种分组-规划-执行-反馈循环实现了高效的规划和稳健的执行。为了评估这些能力，我们引入了CLiMBench，一个具有挑战性的装配任务的异构多机器人基准。我们的实验表明，CLiMRS超越了最佳基线，复杂任务的效率提高了超过40%，同时在简单任务上的成功率未受损。总体而言，我们的结果表明，利用人类启发的群体形成和谈判原则显著增强了异构多机器人协作的效率。我们的代码可在此处获取：https://github.com/song-siqi/CLiMRS。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
