LLM让多机器人系统更智能，从任务分配到人机交互全面升级。
Large Language Models for Multi-Robot Systems: A Survey
- 用大模型统一处理任务分配、规划与执行，实现跨层级智能协同。
- 在家庭、建筑、追踪等场景验证了多机器人系统的高效协作能力。
- 适合关注智能机器人系统设计与落地的研究者参考。
大语言模型（LLMs）的快速发展为多机器人系统（MRS）带来了新机遇，提升了通信、任务分配与规划、人机交互能力。与传统单机器人或多智能体系统不同，MRS面临协调、可扩展性与现实适应性等独特挑战。本文首次系统综述了LLM在MRS中的集成应用，按高层任务分配、中层运动规划、低层动作生成及人机干预进行分类。涵盖家庭服务、建筑施工、编队控制、目标追踪和机器人游戏等多个领域，展示了LLM在多样化场景中的适用性与变革潜力。同时分析了制约因素，包括数学推理能力不足、幻觉问题、延迟及缺乏稳健评估体系。最后提出未来研究方向，强调微调技术、推理方法与专用模型的发展。本综述旨在指导基于LLM的MRS智能化与实际部署。论文列表持续更新于开源GitHub仓库。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) has opened new possibilities in Multi-Robot Systems (MRS), enabling enhanced communication, task allocation and planning, and human-robot interaction. Unlike traditional single-robot and multi-agent systems, MRS poses unique challenges, including coordination, scalability, and real-world adaptability. This survey provides the first dedicated review of LLM integration into MRS. It systematically categorizes their applications across high-level task allocation, mid-level motion planning, low-level action generation, and human intervention. We highlight key applications in diverse domains, such as household robotics, construction, formation control, target tracking, and robot games, showcasing the versatility and transformative potential of LLMs in MRS. Furthermore, we examine the challenges that limit adapting LLMs to MRS, including mathematical reasoning limitations, hallucination, latency issues, and the need for robust benchmarking systems. Finally, we outline opportunities for future research, emphasizing advancements in fine-tuning, reasoning techniques, and task-specific models. This survey aims to guide researchers in the intelligence and real-world deployment of MRS powered by LLMs. Given the rapidly evolving nature of research in the field, we continuously update the paper list in the open-source GitHub repository.
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