Files and links (1)
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Large Language Models for Multi-Robot Systems: A Survey
06 Feb 2025
Abstract
The rapid advancement of Large Language Models (LLMs) has opened new
possibilities in Multi-Robot Systems (MRS), enabling enhanced communication,
task 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
comprehensive exploration 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 in 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. Based on the fast-evolving
nature of research in the field, we keep updating the papers in the open-source
Github repository.
Metrics
23 Record Views
Details
- Title
- Large Language Models for Multi-Robot Systems: A Survey
- Creators
- Peihan LiZijian AnShams AbrarLifeng Zhou
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Other Identifier
- 991022027422004721