RoboBrain2.0-7B: Mirror of https://huggingface.co/BAAI/RoboBrain2.0-7B

---
license: apache-2.0
---

# RoboBrain 2.0: See Better. Think Harder. Do Smarter.

<p align="center">
        </a>&nbsp&nbsp⭐️ <a href="https://superrobobrain.github.io/">Project</a></a>&nbsp&nbsp | &nbsp&nbsp⭐️ <a href="https://github.com/FlagOpen/RoboBrain2.0">Github</a>&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://www.modelscope.cn/models/BAAI/RoboBrain2.0-7B/files/">ModelScope</a>&nbsp&nbsp | &nbsp&nbsp📑 <a href="">Technical Report (Coming soon)</a>&nbsp&nbsp | &nbsp&nbsp💬 <a href="./assets/wechat.png">WeChat</a>
</p>
<p align="center">
        </a>&nbsp&nbsp🎯 <a href="https://github.com/FlagOpen/RoboOS">RoboOS</a>: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain.
</p>
<p align="center">
</a>&nbsp&nbsp🌍 <a href="https://github.com/FlagOpen/RoboBrain">RoboBrain 1.0</a>: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
</p>

## 🔥 Overview
We are excited to introduce **RoboBrain 2.0**, the most powerful open-source embodied brain model to date. Compared to its predecessor, RoboBrain1.0, our latest version significantly advances multi-agent task planning, spatial reasoning, and closed-loop execution. A detailed technical report will be released soon.

## 🗞️ News
- **`2025-06-06`**: 🤗 [RoboBrain 2.0-7B](https://huggingface.co/BAAI/RoboBrain2.0-7B) model checkpoint has been released in Huggingface..
- **`2025-06-06`**: 🔥 We're excited to announce the release of our more powerful [RoboBrain 2.0](https://superrobobrain.github.io/).
- **`2025-04-11`**: 🎉 [RoboBrain 1.0](https://github.com/FlagOpen/RoboBrain/) was selected for CVPR 2025's official [Embodied AI Trends Commentary](https://cvpr.thecvf.com/Conferences/2025/News/AI_Enhanced_Robotics).
- **`2025-02-27`**: 🌍 [RoboBrain 1.0](http://arxiv.org/abs/2502.21257/) was accepted to CVPR2025.

## 📆 Todo
- [x] Release model checkpoint for RoboBrain 2.0-7B
- [x] Release quick inference example for RoboBrain 2.0
- [ ] Release training codes for RoboBrain 2.0
- [ ] Release model checkpoint for RoboBrain 2.0-32B

## 🚀 Features
**RoboBrain 2.0** supports ***interactive reasoning*** with long-horizon planning and closed-loop feedback, ***spatial perception*** for precise point and bbox prediction from complex instructions, ***temporal perception*** for future trajectory estimation, and ***scene reasoning*** through real-time structured memory construction and update.

## ⭐️ Architecture
**RoboBrain 2.0** supports ***multi-image***, ***long video***, and ***high-resolution visual inputs***, along with complex task instructions and structured ***scene graphs*** on the language side. Visual inputs are processed via a Vision Encoder and MLP Projector, while textual inputs are tokenized into a unified token stream. All inputs are fed into a ***LLM Decoder*** that performs ***long-chain-of-thought reasoning*** and outputs structured plans, spatial relations, and both ***relative*** and ***absolute coordinates***.

## 🤗 Model Zoo

| Models               | Checkpoint                                                     | Description                                                | 
|----------------------|----------------------------------------------------------------|------------------------------------------------------------|
| RoboBrain 2.0 7B      | [🤗 BAAI/RoboBrain2.0-7B](https://huggingface.co/BAAI/RoboBrain2.0-7B)   | 7B parameter version of the RoboBrain2.0                   | 
| RoboBrain 2.0 32B      | [🤗 BAAI/RoboBrain2.0-32B](https://huggingface.co/BAAI/RoboBrain2.0-32B)   | 32B parameter version of the RoboBrain2.0 *(Coming soon)*

## 🛠️ Setup

```bash
# clone repo.
git clone https://github.com/FlagOpen/RoboBrain2.0.git
cd RoboBrain

# build conda env.
conda create -n robobrain2 python=3.10
conda activate robobrain2
pip install -r requirements.txt
```

## 🤖 Simple Inference

### 1. Predict without thinking
```python
from inference import SimpleInference

model = SimpleInference("BAAI/RoboBrain2.0-7B")

prompt = "What is shown in this image?"
image = "http://images.cocodataset.org/val2017/000000039769.jpg"

pred = model.inference(prompt, image, enable_thinking=False, do_sample=True)
print(f"Prediction:\n{pred}")

"""
Prediction: (as an example)
{
        'thinking': '', 
        'answer': 'Two cats sleeping side by side on a couch.'
}
"""
```

### 2. Predict with thinking (Beta)
```python
from inference import SimpleInference

model = SimpleInference("BAAI/RoboBrain2.0-7B")

prompt = "What is shown in this image?"
image = "http://images.cocodataset.org/val2017/000000039769.jpg"

pred = model.inference(prompt, image, enable_thinking=True, do_sample=True)
print(f"Prediction:\n{pred}")

"""
Prediction: (as an example)
{
        'thinking': 'Upon examining the visual input, I observe two cats resting comfortably on a pink blanket that covers a couch or sofa. The cats are lying side by side, with one on top of the other, indicating their relaxed state and possibly their close bond. Their positions suggest they feel safe and at ease in their environment.\n\nWith my advanced visual processing capabilities, I can identify various objects within this scene, such as the pink blanket beneath the cats and the couch they are lying on. Additionally, there appear to be remote controls nearby, potentially situated on or near the couch, which further confirms that this is an indoor setting where people relax and spend leisure time.', 
        'answer': 'The image shows two cats lying on a pink blanket on a couch.'
}
"""
```

## 😊 More Results

**Benchmark comparison across spatial reasoning and task planning.** ***RoboBrain2.0-32B*** achieves state-of-the-art performance on four key embodied intelligence benchmarks: ***BLINK-Spatial***, ***CV-Bench***, ***EmbSpatial***, and ***RefSpatial***. It not only outperforms leading open-source models such as o4-mini and Qwen2.5-VL, but also surpasses closed-source models like Gemini 2.5 Pro and Claude Sonnet 4 — especially in the challenging ***RefSpatial*** benchmark, where ***RoboBrain2.0*** shows a >50% absolute improvement.

## 📑 Citation
If you find this project useful, welcome to cite us.
```bib
@article{RoboBrain 2.0 Technical Report,
    title={RoboBrain 2.0 Technical Report},
    author={BAAI RoboBrain Team},
    journal={arXiv preprint arXiv:TODO},
    year={2025}
}

@article{RoboBrain 1.0,
    title={Robobrain: A unified brain model for robotic manipulation from abstract to concrete},
    author={Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and others},
    journal={arXiv preprint arXiv:2502.21257},
    year={2025}
}

@article{RoboOS,
    title={RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration},
    author={Tan, Huajie and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Lyu, Yaoxu and Cao, Mingyu and Wang, Zhongyuan and Zhang, Shanghang},
    journal={arXiv preprint arXiv:2505.03673},
    year={2025}
}

@article{RoboRefer,
    title={RoboRefer: Towards Spatial Referring with Reasoning in Vision-Language Models for Robotics},
    author={Zhou, Enshen and An, Jingkun and Chi, Cheng and Han, Yi and Rong, Shanyu and Zhang, Chi and Wang, Pengwei and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and Zhang, Shanghang},
    journal={arXiv preprint arXiv:2506.04308},
    year={2025}
}

@article{Reason-RFT,
    title={Reason-rft: Reinforcement fine-tuning for visual reasoning},
    author={Tan, Huajie and Ji, Yuheng and Hao, Xiaoshuai and Lin, Minglan and Wang, Pengwei and Wang, Zhongyuan and Zhang, Shanghang},
    journal={arXiv preprint arXiv:2503.20752},
    year={2025}
}

@article{Code-as-Monitor,
    title={Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detection},
    author={Zhou, Enshen and Su, Qi and Chi, Cheng and Zhang, Zhizheng and Wang, Zhongyuan and Huang, Tiejun and Sheng, Lu and Wang, He},
    journal={arXiv preprint arXiv:2412.04455},
    year={2024}
}
```

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