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---
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
---
<p align="center">
<img src="https://robo-bench.github.io/static/images/log/R1.png" alt="RoboBench Logo" width="120"/>
</p>
<h1 align="center" style="font-size:2.5em;">RoboBench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain</h1>
<div align="center">
[](https://arxiv.org/abs/2510.17801v1)
[](https://github.com/lyl750697268/RoboBench)
[](https://robo-bench.github.io/)
[](https://creativecommons.org/licenses/by/4.0/)
</div>
## 📋 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)
---
<div align="center">
**Made with ❤️ by the RoboBench Team**
</div>
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