⭐️ Project Page | 🤗 Hugging Face | 🤖 ModelScope
📖 Wisemodel | 📑 2.0 Technical Report | 💬 WeChat & RedNote
🔥 Robo-Dopamine: Research on Dense Temporal Value Estimation Capability for RoboBrain 2.5.
🔥 RoboTracer: Research on Native 3D Spatial Reasoning Capability for RoboBrain 2.5.
🎯 RoboOS: An Efficient Open-Source Multi-Robot Coordination System for RoboBrain 2.0.
⭐️ Reason-RFT: Core Post-Training Strategy for Embodied Visual Reasoning in RoboBrain 2.0.
🌍 RoboBrain 1.0: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.
💬 If you have any questions, feel free to contact us via WeChat or RedNote.
RoboBrain-2.5 is a next-generation Embodied AI foundation model that significantly evolves its predecessor's core capabilities in general perception, spatial reasoning, and temporal modeling through extensive training on high-quality spatiotemporal data. It achieves a paradigm shift in 3D Spatial Reasoning, transitioning from 2D relative points to predicting 3D coordinates with depth information, understanding absolute metric constraints, and generating complete manipulation trajectories tailored for complex tasks with physical constraints. Furthermore, it establishes a breakthrough in Temporal Value Prediction by constructing a General Reward Modeling Method that provides dense progress tracking and multi-granular execution state estimation across varying viewpoints. This empowers VLA reinforcement learning with immediate, dense feedback signals, enabling robots to achieve high task success rates and robustness in fine-grained manipulation scenarios.
RoboBrain 2.0, the previously most powerful open-source embodied brain model. 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.
2026-01-09: 🤗 We released the RoboBrain 2.5-8B checkpoints on Hugging Face: RoboBrain 2.5-8B-NV and RoboBrain 2.5-8B-MT. The two variants share the same architecture and training data with similar performance, but were trained on different clusters: NV on NVIDIA GPU cluster, and MT on Moore-Threads GPU cluster.2025-12-30: 🔥 We released Robo-Dopamine, a deep research on Dense Temperal Value Estimation Capability for RoboBrain 2.5.2025-12-16: 🔥 We released RoboTracer, a deep research on Native 3D Spatial Reasoning for RoboBrain 2.5.2025-09-29: 🤖 We released a unified cross-embodiment VLA model RoboBrain-X0-Preview based on RoboBrain 2.0 (3B version) on CoRL 2025.2025-09-18: 🔥 Reason-RFT (Core Post-Training Strategy for RoboBrain2.0) gets accepted to NeurIPS 2025.2025-07-23: 🤗 RoboBrain 2.0-3B model checkpoint has been also released in Huggingface.2025-07-03: 🤗 RoboBrain 2.0-32B model checkpoint has been released in Huggingface.2025-06-11: 💡 We optimized the inference pipeline for multi-task applications in RoboBrain 2.0. Please refer to Simple Inference for quick usage (general & embodied).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 CVPR 2025.
- Release model checkpoint for RoboBrain 2.0-3B
- Release model checkpoint for RoboBrain 2.0-7B
- Release model checkpoint for RoboBrain 2.0-32B
- Release quick inference example for RoboBrain 2.0
- Release training and evaluation codes for RoboBrain 2.0
- Release model checkpoint for RoboBrain 2.5-8B
- Release model checkpoint for RoboBrain 2.5-32B
Compared to version 2.0, RoboBrain-2.5 achieves a leap in spatial perception and reasoning capabilities:
- From 2D to 3D: Upgraded from predicting coordinate points on 2D images to predicting coordinate points with depth information in 3D space (3D Spatial Referring).
- Relative to Absolute: Evolved from understanding relative spatial relationships to measuring absolute 3D spatial metric information (3D Spatial Measuring). The model can comprehend precise physical constraint instructions (e.g., "hovering 1-5 cm above").
- Point to Trace: Advanced from predicting a single target point for pick-and-place to predicting a series of key points that describe the complete manipulation process (3D Spatial Trace), naturally possessing spatial planning capabilities with 3D absolute metrics.
RoboBrain-2.5 makes significant progress in temporal modeling by constructing a General Reward Model (GRM):
- Dense Progress Prediction: Capable of multi-granularity task progress prediction across different tasks, viewpoints, and embodiments.
- Execution State Estimation: Understands task goals and estimates various states during execution (e.g., success, failure, error occurrence).
- Empowering VLA Reinforcement Learning: Provides real-time, dense feedback signals and rewards for VLA (Vision-Language-Action) reinforcement learning. With only one demonstration, it achieves a task success rate of 95%+ in complex, fine-grained manipulations.
RoboBrain 2.5 also maintains the three core capabilities of version 2.0, which 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.
| Models | Checkpoint | Description |
|---|---|---|
| RoboBrain 2.0 3B | 🤗 BAAI/RoboBrain2.0-3B | 3B parameter version of the RoboBrain2.0 |
| 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 |
| RoboBrain 2.5 8B | 🤗 BAAI/RoboBrain2.5-8B-NV | 8B parameter version of the RoboBrain2.5 |
| RoboBrain 2.5 8B | 🤗 BAAI/RoboBrain2.5-8B-MT | 8B parameter version of the RoboBrain2.5 |
# clone repo.
git clone https://github.com/FlagOpen/RoboBrain2.5.git
cd RoboBrain2.5
# build conda env.
conda create -n robobrain2_5 python=3.10
conda activate robobrain2_5
pip install -r requirements.txtfrom inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example:
prompt = "What is shown in this image?"
image = "http://images.cocodataset.org/val2017/000000039769.jpg"
pred = model.inference(prompt, image, task="general")
print(f"Prediction:\n{pred}")from inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example:
prompt = "the person wearing a red hat"
image = "./assets/demo/grounding.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
pred = model.inference(prompt, image, task="grounding", plot=True, do_sample=False)
print(f"Prediction:\n{pred}")from inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example:
prompt = "the affordance area for holding the cup"
image = "./assets/demo/affordance.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
pred = model.inference(prompt, image, task="pointing", plot=True, do_sample=False)
print(f"Prediction:\n{pred}")from inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example:
prompt = "Identify spot within the vacant space that's between the two mugs"
image = "./assets/demo/pointing.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
pred = model.inference(prompt, image, task="pointing", plot=True, do_sample=True)
print(f"Prediction:\n{pred}")from inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example 1:
prompt_1 = "Identify spot within toilet in the house"
image = "./assets/demo/navigation.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
pred = model.inference(prompt_1, image, task="pointing", plot=True, do_sample=True)
print(f"Prediction:\n{pred}")
# Example 2:
prompt_2 = "Identify spot within the sofa in the house"
image = "./assets/demo/navigation.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
pred = model.inference(prompt_2, image, task="pointing", plot=True, do_sample=True)
print(f"Prediction:\n{pred}")from inference import UnifiedInference
model = UnifiedInference("BAAI/RoboBrain2.5-8B-NV")
# Example:
prompt = "reach for the banana on the plate"
image = "./assets/demo/trajectory.jpg"
# Visualization results will be saved to ./result, if `plot=True`.
# Output is formatted as a list of tuples, i.e., [(x1, y1, d1), (x2, y2, d2), ...],
# where each tuple contains the x and y coordinates and the depth of the point.
pred = model.inference(prompt, image, task="trajectory", plot=True, do_sample=False)
print(f"Prediction:\n{pred}")We highly recommend referring to Robo-Dopamine for detailed usage instructions.
# clone Robo-Dopamine repo.
git clone https://github.com/FlagOpen/Robo-Dopamine.git
cd Robo-Dopamineimport os
from examples.inference import GRMInference
# model = GRMInference("tanhuajie2001/Robo-Dopamine-GRM-3B")
model = GRMInference("BAAI/RoboBrain2.5-8B-NV")
TASK_INSTRUCTION = "organize the table"
BASE_DEMO_PATH = "./examples/demo_table"
GOAL_IMAGE_PATH = "./examples/demo_table/goal_image.png"
OUTPUT_ROOT = "./results"
output_dir = model.run_pipeline(
cam_high_path = os.path.join(BASE_DEMO_PATH, "cam_high.mp4"),
cam_left_path = os.path.join(BASE_DEMO_PATH, "cam_left_wrist.mp4"),
cam_right_path = os.path.join(BASE_DEMO_PATH, "cam_right_wrist.mp4"),
out_root = OUTPUT_ROOT,
task = TASK_INSTRUCTION,
frame_interval = 30,
batch_size = 1,
goal_image = GOAL_IMAGE_PATH,
eval_mode = "incremental",
visualize = True
)
print(f"Episode ({BASE_DEMO_PATH}) processed with Incremental-Mode. Output at: {output_dir}")
We adopted the distributed training framework FlagScale developed by the Framework R&D team of BAAI for training. The training can be launched in the following steps:
You can refer to the QuickStart.md to train the base instruct model or finetune the RoboBrain2.5.
RoboBrain2.5 is also compatible with the official training code for Qwen3VL. Please refer to qwen-vl-finetune.
If you find this project useful, welcome to cite us.
@article{tan2025robo,
title={Robo-Dopamine: General Process Reward Modeling for High-Precision Robotic Manipulation},
author={Tan, Huajie and Chen, Sixiang and Xu, Yijie and Wang, Zixiao and Ji, Yuheng and Chi, Cheng and Lyu, Yaoxu and Zhao, Zhongxia and Chen, Xiansheng and Co, Peterson and others},
journal={arXiv preprint arXiv:2512.23703},
year={2025}
}
@article{zhou2025robotracer,
title={RoboTracer: Mastering Spatial Trace with Reasoning in Vision-Language Models for Robotics},
author={Zhou, Enshen and Chi, Cheng and Li, Yibo and An, Jingkun and Zhang, Jiayuan and Rong, Shanyu and Han, Yi and Ji, Yuheng and Liu, Mengzhen and Wang, Pengwei and others},
journal={arXiv preprint arXiv:2512.13660},
year={2025}
}
@article{RoboBrain2.0TechnicalReport,
title={RoboBrain 2.0 Technical Report},
author={BAAI RoboBrain Team},
journal={arXiv preprint arXiv:2507.02029},
year={2025}
}
@article{RoboBrain1.0,
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{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{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}
}
