Large Language Models (LLMs) have demonstrated significant reasoning capabilities in robotic systems. However, their deployment in multi-robot systems remains fragmented and struggles to handle complex task dependencies and parallel execution. This study introduces the DART-LLM (Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models) system, designed to address these challenges. DART-LLM utilizes LLMs to parse natural language instructions, decomposing them into multiple subtasks with dependencies to establish complex task sequences, thereby enhancing efficient coordination and parallel execution in multi-robot systems. The system includes the QA LLM module, Breakdown Function modules, Actuation module, and a Vision-Language Model (VLM)-based object detection module, enabling task decomposition and execution from natural language instructions to robotic actions. Experimental results demonstrate that DART-LLM excels in handling long-horizon tasks and collaborative tasks with complex dependencies. Even when using smaller models like Llama 3.1 8B, the system achieves good performance, highlighting DART-LLM's robustness in terms of model size. Please refer to the project website \url{https://wyd0817.github.io/project-dart-llm/} for videos and code.
View on arXiv@article{wang2025_2411.09022,
title={ DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models },
author={ Yongdong Wang and Runze Xiao and Jun Younes Louhi Kasahara and Ryosuke Yajima and Keiji Nagatani and Atsushi Yamashita and Hajime Asama },
journal={arXiv preprint arXiv:2411.09022},
year={ 2025 }
}