README.md

metadata

title: 'DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution'
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: gradio
app_file: app.py
pinned: false
license: llama3.1

DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models (Spaces)

This project is part of the Moonshot Café Project

Yongdong Wang^1,*, Runze Xiao¹, Jun Younes Louhi Kasahara¹, Ryosuke Yajima¹, Keiji Nagatani^{1, 2}, Atsushi Yamashita³, Hajime Asama⁴

¹Graduate School of Engineering, The University of Tokyo
²Faculty of Systems and Information Engineering, University of Tsukuba
³Graduate School of Frontier Sciences, The University of Tokyo
⁴Tokyo College, The University of Tokyo

*Corresponding author: [email protected]

Overview

This Hugging Face Space hosts DART-LLM, a QLoRA-fine-tuned meta-llama/Llama-3.1-8B model specialized in construction robotics. It demonstrates converting natural language robot commands into structured JSON tasks, supporting detailed multi-robot coordination, spatial reasoning, and action planning.

Quick Start

1. Enter your robot command in the provided interface.
2. Click Generate Tasks.
3. Review the structured JSON output describing the robot task sequence.

Citation

If you use this work, please cite:

@article{wang2024dart,
  title={Dart-llm: Dependency-aware multi-robot task decomposition and execution using large language models},
  author={Wang, Yongdong and Xiao, Runze and Kasahara, Jun Younes Louhi and Yajima, Ryosuke and Nagatani, Keiji and Yamashita, Atsushi and Asama, Hajime},
  journal={arXiv preprint arXiv:2411.09022},
  year={2024}
}