Update Readme.md

README.md

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 ---
-title: Robot Task Planning - Llama 3.1 8B
+title: "DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution"
 emoji: 🤖
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 license: llama3.1
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-# 🤖 Robot Task Planning - Llama 3.1 8B (ZeroGPU)
-
-This Space demonstrates a fine-tuned version of Meta's **Llama 3.1 8B** model specialized for **robot task planning** using QLoRA (4-bit quantization + LoRA) technique.
-
-## 🚀 Hardware: ZeroGPU
-
-This Space uses **ZeroGPU** - dynamic GPU allocation with Nvidia H200:
-- **Free** for HuggingFace users
-- **Dynamic allocation** - GPU resources allocated on-demand
-- **High performance** - H200 offers superior performance
-- **60-second duration** per request
-
-## 🎯 Purpose
-
-Convert natural language commands into structured task sequences for construction robots including:
-- **Excavators** - Digging, loading, positioning
-- **Dump Trucks** - Material transport, loading, unloading  
-- **Multi-robot Coordination** - Complex task dependencies
-
-## 🔗 Model
-
-**Fine-tuned Model**: [YongdongWang/llama-3.1-8b-dart-qlora](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora)
-
-**Base Model**: [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
-
-## ✨ Features
-
-- 🎮 **Interactive Chat Interface** - Real-time robot command processing
-- ⚙️ **Configurable Generation** - Adjust temperature, top-p, max tokens
-- 📝 **Example Commands** - Pre-built scenarios to get started
-- 🚀 **Optimized Performance** - 4-bit quantization for efficient inference
-- 📊 **Structured Output** - JSON-formatted task sequences
-- ⚡ **ZeroGPU Powered** - Dynamic GPU allocation for free users
-
-## 🚀 Usage
-
-1. **Input**: Natural language robot commands
-   ```
-   "Deploy Excavator 1 to Soil Area 1 for excavation"
-   ```
-
-2. **Output**: Structured task sequences
-   ```json
-   {
-     "tasks": [
-       {
-         "robot": "Excavator_1",
-         "action": "move_to",
-         "target": "Soil_Area_1",
-         "duration": 30
-       },
-       {
-         "robot": "Excavator_1", 
-         "action": "excavate",
-         "target": "Soil_Area_1",
-         "duration": 120
-       }
-     ]
-   }
-   ```
-
-## 🛠️ Technical Details
-
-- **Architecture**: Llama 3.1 8B + QLoRA adapters
-- **Quantization**: 4-bit (NF4) with double quantization
-- **Framework**: Transformers + PEFT + BitsAndBytesConfig
-- **Hardware**: ZeroGPU (Dynamic Nvidia H200)
-
-## ⚡ Performance Notes
-
-- **First Generation**: 5-10 seconds (GPU allocation + model loading)
-- **Subsequent Generations**: 2-5 seconds per response
-- **Memory Usage**: ~8GB VRAM with 4-bit quantization
-- **Context Length**: Up to 2048 tokens
-- **GPU Duration**: 60 seconds per request
-
-## 📚 Example Commands
-
-Try these robot commands:
-
-- `"Deploy Excavator 1 to Soil Area 1 for excavation"`
-- `"Send Dump Truck 1 to collect material, then unload at storage"`
-- `"Coordinate multiple excavators across different areas"`
-- `"Create evacuation sequence for all robots from dangerous zone"`
-
-## 🔬 Research Applications
-
-This model demonstrates:
-- **Natural Language → Robot Planning** translation
-- **Multi-agent Task Coordination** 
-- **Efficient LLM Fine-tuning** with QLoRA
-- **Real-time Robot Command Processing**
-- **ZeroGPU Integration** for scalable deployment
-
-## 📄 License
-
-This project uses Meta's Llama 3.1 license. Please review the license terms before use.
-
-## 🤝 Contributing
-
-For issues, improvements, or questions about the model, please visit the [model repository](https://huggingface.co/YongdongWang/llama-3.1-8b-dart-qlora).
+<div align="center">
+<h1>DART-LLM: Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models (Spaces)</h1>
+<div class="project-info">
+    This project is part of the <a href="https://moonshot-cafe-project.org/en/" target="_blank">Moonshot Café Project</a>
+</div>
+<div class="authors">
+  <a href="https://researchmap.jp/wangyongdong?lang=en" target="_blank">Yongdong Wang</a><sup class="org-1">1,*</sup>,
+  Runze Xiao<sup class="org-1">1</sup>,
+  <a href="https://www.robot.t.u-tokyo.ac.jp/~louhi_kasahara/index-e.html" target="_blank">Jun Younes Louhi Kasahara</a><sup class="org-1">1</sup>,
+  <a href="https://researchmap.jp/r-yaj?lang=en" target="_blank">Ryosuke Yajima</a><sup class="org-1">1</sup>,
+  <a href="http://k-nagatani.org/" target="_blank">Keiji Nagatani</a><sup class="org-1">1</sup><sup class="org-2">, 2</sup>,
+  <a href="https://www.robot.t.u-tokyo.ac.jp/~yamashita/" target="_blank">Atsushi Yamashita</a><sup class="org-3">3</sup>,
+  <a href="https://www.robot.t.u-tokyo.ac.jp/asamalab/en/members/asama/biography.html" target="_blank">Hajime Asama</a><sup class="org-4">4</sup>
+</div>
+<div class="affiliations">
+  <sup class="org-1">1</sup>Graduate School of Engineering, The University of Tokyo<br>
+  <sup class="org-2">2</sup>Faculty of Systems and Information Engineering, University of Tsukuba<br>
+  <sup class="org-3">3</sup>Graduate School of Frontier Sciences, The University of Tokyo<br>
+  <sup class="org-4">4</sup>Tokyo College, The University of Tokyo
+</div>
+<div class="corresponding-author">
+  *Corresponding author: <a href="mailto:wangyongdong@robot.t.u-tokyo.ac.jp">[email protected]-tokyo.ac.jp</a>
+</div>
+<div align="center">
+  <a href="https://arxiv.org/pdf/2411.09022" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/arXiv-2411.09022-b31b1b" alt="arXiv Badge">
+  </a>
+  <a href="https://github.com/wyd0817/QA_LLM_Module" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/QA_LLM_Module-GitHub-blue" alt="QA LLM Module GitHub Badge">
+  </a>
+  <a href="https://huggingface.co/datasets/YongdongWang/dart_llm_tasks" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/Dataset-Hugging_Face-blue" alt="Dataset Badge">
+  </a>
+  <a href="https://huggingface.co/spaces/YongdongWang/DART-LLM-Llama3.1-8b" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/Spaces-DART--LLM--Llama3.1--8b-lightgrey" alt="Spaces Badge">
+  </a>
+  <a href="https://www.youtube.com/watch?v=p3A-yg3yv0Q" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/Video-YouTube-red" alt="Video Badge">
+  </a>
+  <a href="https://www.youtube.com/watch?v=T3M94hP8NFQ" target="_blank" rel="noopener noreferrer">
+    <img src="https://img.shields.io/badge/Real_Robot-YouTube-orange" alt="Real Robot Badge">
+  </a>
+</div>
+
+## 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:
+
+```bibtex
+@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}
+}
+```