metadata
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
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
Made with ❤️ by the RoboBench Team