README.md

---
license: cc-by-4.0
task_categories:
- robotics
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: benchmark
  data_files:
  - split: single_arm
    path: 3_generalized_planning/cross_embodiment/single_arm/questions.json
---

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  <img src="https://robo-bench.github.io/static/images/log/R1.png" alt="RoboBench Logo" width="120"/>
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<h1 align="center" style="font-size:2.5em;">RoboBench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain</h1>

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[![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/lyl750697268/RoboBench)
[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://robo-bench.github.io/)
[![License](https://img.shields.io/badge/License-CC%20BY%204.0-blue)](https://creativecommons.org/licenses/by/4.0/)

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## 📋 Overview

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.

## 🎯 Key Features

- **🧠 Comprehensive Evaluation**: Covers multiple aspects of embodied intelligence
- **📊 Rich Dataset**: Contains thousands of carefully curated examples
- **🔬 Scientific Rigor**: Designed with research-grade evaluation metrics
- **🌐 Multimodal**: Supports text, images, and video data
- **🤖 Robotics Focus**: Specifically tailored for robotic applications

## 📊 Dataset Statistics

| Category | Count | Description |
|----------|-------|-------------|
| **Total Samples** | 6092 | Comprehensive evaluation dataset |
| **Image Samples** | 1400 | High-quality visual data |
| **Video Samples** | 3142 | Temporal & Planning reasoning examples |

## 🏗️ Dataset Structure

```
RoboBench/
├── 1_instruction_comprehension/    # Instruction understanding tasks
├── 2_perception_reasoning/         # Visual perception and reasoning
├── 3_generalized_planning/         # Cross-domain planning tasks
├── 4_affordance_reasoning/         # Object affordance understanding
├── 5_error_analysis/               # Error analysis and debugging
└──system_prompt.json.              # Every task system prompts
```


## 🔬 Research Applications

This benchmark is designed for researchers working on:

- **Multimodal Large Language Models**
- **Embodied AI Systems**
- **Robotic Intelligence**
- **Computer Vision**
- **Natural Language Processing**

## 📚 Citation

If you use RoboBench in your research, please cite our paper:

```bibtex
@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/lyl750697268/RoboBench) for more details.

## 📄 License

This dataset is released under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/).

## 🔗 Links

- **📄 Paper**: [arXiv:2510.17801](https://arxiv.org/abs/2510.17801v1)
- **🏠 Project Page**: [https://robo-bench.github.io/](https://robo-bench.github.io/)
- **💻 GitHub**: [https://github.com/lyl750697268/RoboBench](https://github.com/lyl750697268/RoboBench)

---

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**Made with ❤️ by the RoboBench Team**

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