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[CVPR 2025] RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete. Official Repository.
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| 2026-07-24 | 559 |
| 2026-07-25 | 559 |
| 2026-07-28 | 559 |
| 2026-07-30 | 559 |
| 2026-08-06 | 559 |
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# [CVPR 25] RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
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</a>  ⭐️ <a href="https://superrobobrain.github.io/">Project</a></a>   |   🤗 <a href="https://huggingface.co/BAAI/RoboBrain/">Hugging Face</a>   |   🤖 <a href="https://www.modelscope.cn/models/BAAI/RoboBrain/files/">ModelScope</a>   |   🌎 <a href="https://github.com/FlagOpen/ShareRobot">Dataset</a>   |   📑 <a href="http://arxiv.org/abs/2502.21257">Paper</a>   |   💬 <a href="./assets/wechat.png">WeChat</a>
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</a>  🔥🔥 <a href="https://github.com/FlagOpen/RoboBrain2.0"><strong>RoboBrain 2.0</strong></a><strong>: More powerful version of RoboBrain: See Better. Think Harder. Do Smarter.</strong>
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</a>  🎯 <a href="https://github.com/FlagOpen/RoboOS">RoboOS</a>: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain.
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</a>  🎯 <a href="https://github.com/tanhuajie/Reason-RFT">Reason-RFT</a>: Exploring Efficient RFT Paradigm to Enhance RoboBrain's Visual Reasoning Capabilities.
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## 🔥 Overview
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: **(1) Planning Capability**, which involves decomposing complex manipulation instructions into manageable sub-tasks; **(2) Affordance Perception**, the ability to recognize and interpret the affordances of interactive objects; and **(3) Trajectory Prediction**, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain's core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot's diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities. Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various robotic tasks, highlighting its potential to advance robotic brain capabilities.
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<img src="./assets/overview.png" />
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## 🚀 Features
This repository supports:
- **`Data Preparation`**: Please refer to [Dataset Preparation](https://github.com/FlagOpen/ShareRobot) for how to prepare the dataset.
- **`Training for RoboBrain`**: Please refer to [Training Section](#Training) for the usage of training scripts.
- **`Support HF/VLLM Inference`**: Please see [Inference Section](#Inference), now we support inference with [VLLM](https://github.com/vllm-project/vllm).
- **`Evaluation for RoboBrain`**: Please refer to [Evaluation Section](#Evaluation) for how to prepare the benchmarks.
- **`ShareRobot Generation`**: Please refer to [ShareRobot](https://github.com/FlagOpen/ShareRobot) for details.
## 🗞️ 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://github.com/FlagOpen/RoboBrain2.0).
- **`2025-04-11`**: 🎉 [RoboBrain](https://github.com/FlagOpen/RoboBrain/) was selected for CVPR 2025's official [Embodied AI TrExcerpt of 17,788 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c1bd1ec42c456077, topic:robotics, topic:embodied-ai, desc:manipulation