MHRC：基于大型语言模型的闭环去中心化多异构机器人协作
MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models
AI总结：
本文提出了一种基于大型语言模型的去中心化多异构机器人协作框架，通过自然语言接口、文本反馈机制和思维链提示，支持移动、操作和移动操作三类机器人协同完成探索、运输和整理任务，实验验证了其有效性。
摘要：
大型语言模型（LLMs）与机器人技术的融合显著提升了机器人在感知、认知和任务规划方面的能力。自然语言接口提供了一种统一的方法，用于表达异构机器人的能力差异，促进它们之间的通信，并实现无缝的任务分配与协作。目前，利用LLMs实现去中心化多异构机器人协作任务仍是一个未被充分探索的研究领域。本文提出了一种新颖的框架，利用LLMs实现多个异构机器人之间的去中心化协作。该框架支持三类机器人：移动机器人、操作机器人和移动操作机器人，它们协同完成探索、运输和整理等任务。我们开发了丰富的文本反馈机制和思维链（CoT）提示，以提高任务规划效率和整体系统性能。移动操作机器人能够灵活调整底座位置，确保抓取任务处于最佳状态。操作机器人能够理解任务需求，在必要时寻求协助，并妥善处理物体。同时，移动机器人能够广泛探索环境，绘制物体位置图，并将此信息传达给移动操作机器人，从而提高任务执行效率。我们使用PyBullet对该框架进行了评估，构建了包含三种不同房间布局和三种不同操作任务的场景。我们测试了多种LLM模型，并进行了消融实验以评估不同模块的贡献。实验结果证实了我们提出的框架的有效性和必要性。
英文摘要：
The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language interfaces offers a unified approach for expressing the capability differences of heterogeneous robots, facilitating communication between them, and enabling seamless task allocation and collaboration. Currently, the utilization of LLMs to achieve decentralized multi-heterogeneous robot collaborative tasks remains an under-explored area of research. In this paper, we introduce a novel framework that utilizes LLMs to achieve decentralized collaboration among multiple heterogeneous robots. Our framework supports three robot categories, mobile robots, manipulation robots, and mobile manipulation robots, working together to complete tasks such as exploration, transportation, and organization. We developed a rich set of textual feedback mechanisms and chain-of-thought (CoT) prompts to enhance task planning efficiency and overall system performance. The mobile manipulation robot can adjust its base position flexibly, ensuring optimal conditions for grasping tasks. The manipulation robot can comprehend task requirements, seek assistance when necessary, and handle objects appropriately. Meanwhile, the mobile robot can explore the environment extensively, map object locations, and communicate this information to the mobile manipulation robot, thus improving task execution efficiency. We evaluated the framework using PyBullet, creating scenarios with three different room layouts and three distinct operational tasks. We tested various LLM models and conducted ablation studies to assess the contributions of different modules. The experimental results confirm the effectiveness and necessity of our proposed framework.
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。
