1104

README.md

@@ -23,9 +23,8 @@ configs:
 <div align="center">
 
 [![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2510.17801v1)
-[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/yulin-luo/RoboBench)
+[![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/lyl750697268/RoboBench)
 [![Project Page](https://img.shields.io/badge/Project-Page-green)](https://robo-bench.github.io/)
-[![Official Results](https://img.shields.io/badge/Results-HuggingFace-orange.svg?style=flat&logo=huggingface&logoColor=black)](https://huggingface.co/datasets/lyl010221-pku/RoboBench-Results)
 [![License](https://img.shields.io/badge/License-CC%20BY%204.0-blue)](https://creativecommons.org/licenses/by/4.0/)
 
 </div>
@@ -34,12 +33,6 @@ configs:
 
 RoboBench is a comprehensive evaluation benchmark designed to assess the capabilities of Multimodal Large Language Models (MLLMs) in embodied intelligence tasks. This benchmark provides a systematic framework for evaluating how well these models can understand and reason about robotic scenarios.
 
-This repository contains the released RoboBench benchmark data. Official score tables and model-output JSON files are hosted separately at:
-
-https://huggingface.co/datasets/lyl010221-pku/RoboBench-Results
-
-The results repository includes CSV exports of the paper tables, coverage audits, and released model-output JSON files for Instruction Comprehension, Perception and Reasoning, Generalized Planning, Affordance Reasoning, and Error Analysis.
-
 ## 🎯 Key Features
 
 - **🧠 Comprehensive Evaluation**: Covers multiple aspects of embodied intelligence
@@ -84,20 +77,17 @@ This benchmark is designed for researchers working on:
 If you use RoboBench in your research, please cite our paper:
 
 ```bibtex
-@misc{luo2026robobenchcomprehensiveevaluationbenchmark,
-      title={Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain}, 
-      author={Yulin Luo and Chun-Kai Fan and Menghang Dong and Jiayu Shi and Xiangju Mi and Mengdi Zhao and Bo-Wen Zhang and Cheng Chi and Jiaming Liu and Gaole Dai and Rongyu Zhang and Ruichuan An and Kun Wu and Zhengping Che and Shaoxuan Xie and Guocai Yao and Zhongxia Zhao and Pengwei Wang and Guang Liu and Zhongyuan Wang and Tiejun Huang and Shanghang Zhang},
-      year={2026},
-      eprint={2510.17801},
-      archivePrefix={arXiv},
-      primaryClass={cs.RO},
-      url={https://arxiv.org/abs/2510.17801}, 
+@article{luo2025robobench,
+  title={Robobench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain},
+  author={Luo, Yulin and Fan, Chun-Kai and Dong, Menghang and Shi, Jiayu and Zhao, Mengdi and Zhang, Bo-Wen and Chi, Cheng and Liu, Jiaming and Dai, Gaole and Zhang, Rongyu and others},
+  journal={arXiv preprint arXiv:2510.17801},
+  year={2025}
 }
 ```
 
 ## 🤝 Contributing
 
-We welcome contributions! Please see our [Contributing Guidelines](https://github.com/yulin-luo/RoboBench) for more details.
+We welcome contributions! Please see our [Contributing Guidelines](https://github.com/lyl750697268/RoboBench) for more details.
 
 ## 📄 License
 
@@ -107,8 +97,7 @@ This dataset is released under the [Creative Commons Attribution 4.0 Internation
 
 - **📄 Paper**: [arXiv:2510.17801](https://arxiv.org/abs/2510.17801v1)
 - **🏠 Project Page**: [https://robo-bench.github.io/](https://robo-bench.github.io/)
-- **💻 Code**: [https://github.com/yulin-luo/RoboBench](https://github.com/yulin-luo/RoboBench)
-- **📊 Official results and model outputs**: [https://huggingface.co/datasets/lyl010221-pku/RoboBench-Results](https://huggingface.co/datasets/lyl010221-pku/RoboBench-Results)
+- **💻 GitHub**: [https://github.com/lyl750697268/RoboBench](https://github.com/lyl750697268/RoboBench)
 
 ---