license: apache-2.0
library_name: transformers
pipeline_tag: robotics
RoboBrain 2.0: See Better. Think Harder. Do Smarter.
⭐️ Project | ⭐️ Github | 🤖 ModelScope | 📑 Technical Report | 💬 WeChat
🎯 RoboOS: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain.
🌍 RoboBrain 1.0: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
🔥 Overview
We are excited to introduce RoboBrain 2.0, the most powerful open-source embodied brain model to date. Compared to its predecessor, RoboBrain 1.0, our latest version are designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results in most cases, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied intelligence capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and real-time scene memory). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents.
🗞️ News
2025-07-03: 🤗 RoboBrain 2.0-32B model checkpoint has been released in Huggingface.2025-06-07: 🎉 We highlight the training framework (FlagScale) developed by BAAI Framework R&D team, and the evaluation framework (FlagEvalMM) by BAAI FlagEval team. Both are used for RoboBrain 2.0.2025-06-06: 🤗 RoboBrain 2.0-7B model checkpoint has been released in Huggingface..2025-06-06: 🔥 We're excited to announce the release of our more powerful RoboBrain 2.0.2025-04-11: 🎉 RoboBrain 1.0 was selected for CVPR 2025's official Embodied AI Trends Commentary.2025-02-27: 🌍 RoboBrain 1.0 was accepted to CVPR2025.
📆 Todo
- Release model checkpoint for RoboBrain 2.0-7B
- 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 | 7B parameter version of the RoboBrain2.0 |
| RoboBrain 2.0 32B | 🤗 BAAI/RoboBrain2.0-32B | 32B parameter version of the RoboBrain2.0 |
🛠️ Setup
# 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
Note: Please refer to RoboBrain 2.0 Github for the usage of RoboBrain 2.0
😊 More Results
Benchmark comparison across spatial reasoning and temporal task planning. RoboBrain2.0 achieves state-of-the-art (SOTA) or near-SOTA performance on nine spatial reasoning benchmarks: BLINK-Spatial, CV-Bench, EmbSpatial, RoboSpatial, RefSpatial, SAT, VSI-Bench, Where2Place and ShareRobot-Bench, and three temporal reasoning benchmarks: Multi-Robot-Planning, Ego-Plan2 and RoboBench-Planning, It not only outperforms leading open-source models such as Cosmos-Reason1 and Qwen2.5-VL, but also surpasses closed-source models like Gemini 2.5 Pro, o4-mini and Claude Sonnet 4.
📑 Citation
If you find this project useful, welcome to cite us.
@article{RoboBrain 2.0,
title={RoboBrain 2.0 Technical Report},
author={BAAI RoboBrain Team},
journal={arXiv preprint arXiv:2507.02029},
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{zhou2025roborefer,
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 others},
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}
}