RoboBench: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models as Embodied Brain

📋 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:

@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 for more details.

📄 License

This dataset is released under the Creative Commons Attribution 4.0 International License.

🔗 Links

• 📄 Paper: arXiv:2510.17801
• 🏠 Project Page: https://robo-bench.github.io/
• 💻 GitHub: https://github.com/lyl750697268/RoboBench

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Made with ❤️ by the RoboBench Team