HMCF：一种基于大型语言模型的人机协同多机器人协作框架
HMCF: A Human-in-the-loop Multi-Robot Collaboration Framework Based on Large Language Models
AI总结：
本文提出基于大型语言模型的人机协同多机器人协作框架HMCF，通过集成人类监督、LLM代理和异构机器人优化任务分配与执行，在仿真和真实环境中实现了更高的任务成功率和零样本泛化能力。
摘要：
人工智能（AI）的快速发展使机器人能够以越来越高的精度自主执行复杂任务。然而，多机器人系统（MRSs）在泛化性、异构性和安全性方面面临挑战，尤其是在扩展到如灾难响应等大规模部署时。传统方法通常缺乏泛化能力，需要为新任务和场景进行大量工程开发，且难以管理多样化的机器人。为克服这些限制，我们提出了一种由大型语言模型（LLMs）驱动的人机协同多机器人协作框架（HMCF）。LLMs通过对多样化任务和机器人能力进行推理来增强适应性，而人类监督则确保安全性和可靠性，仅在必要时进行干预。我们的框架无缝集成了人类监督、LLM代理和异构机器人，以优化任务分配和执行。每个机器人配备一个LLM代理，能够理解自身能力，将任务转化为可执行指令，并通过任务验证和人类监督减少幻觉。仿真结果表明，我们的框架优于最先进的任务规划方法，任务成功率提高了4.76%。真实世界测试证明了其强大的零样本泛化特性，以及以最少人工干预处理多样化任务和环境的能力。
英文摘要：
Rapid advancements in artificial intelligence (AI) have enabled robots to performcomplex tasks autonomously with increasing precision. However, multi-robot systems (MRSs) face challenges in generalization, heterogeneity, and safety, especially when scaling to large-scale deployments like disaster response. Traditional approaches often lack generalization, requiring extensive engineering for new tasks and scenarios, and struggle with managing diverse robots. To overcome these limitations, we propose a Human-in-the-loop Multi-Robot Collaboration Framework (HMCF) powered by large language models (LLMs). LLMs enhance adaptability by reasoning over diverse tasks and robot capabilities, while human oversight ensures safety and reliability, intervening only when necessary. Our framework seamlessly integrates human oversight, LLM agents, and heterogeneous robots to optimize task allocation and execution. Each robot is equipped with an LLM agent capable of understanding its capabilities, converting tasks into executable instructions, and reducing hallucinations through task verification and human supervision. Simulation results show that our framework outperforms state-of-the-art task planning methods, achieving higher task success rates with an improvement of 4.76%. Real-world tests demonstrate its robust zero-shot generalization feature and ability to handle diverse tasks and environments with minimal human intervention.
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。
