[CVPR 25] RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete.

🤗 Models

Base Planning Model: The model was trained on general datasets in Stages 1–2 and on the Robotic Planning dataset in Stage 3, which is designed for Planning prediction.
A-LoRA for Affordance: Based on the Base Planning Model, Stage 4 involves LoRA-based training with our Affordance dataset to predict affordance.
T-LoRA for Trajectory: Based on the Base Planning Model, Stage 4 involves LoRA-based training with our Trajectory dataset to predict trajectory. (Coming Soon)

Models	Checkpoint	Description
Planning Model	🤗 Planning CKPTs	Used for Planning prediction in our paper
Affordance (A-LoRA)	🤗 Affordance CKPTs	Used for Affordance prediction in our paper
Trajectory (T-LoRA)	🤗 Trajectory CKPTs	Used for Trajectory prediction in our paper (Coming Soon)

🛠️ Setup

# clone repo.
git clone https://github.com/FlagOpen/RoboBrain.git
cd RoboBrain
# build conda env.
conda create -n robobrain python=3.10
conda activate robobrain
pip install -r requirements.txt

🤖 Training

1. Data Preparation

# Modify datasets for Stage 4_aff, please refer to:
- yaml_path: scripts/train/yaml/stage_4_affordance.yaml

Note: During training, we applied normalization to the bounding boxes, representing them with the coordinates of the top-left and bottom-right corners, and retaining three decimal places for each. The sample format in each json file should be like:

{
    "id": xxxx,
    "image": "testsetv3/Unseen/egocentric/ride/bicycle/bicycle_001662.jpg",
    "conversations": [
        {
            "value": "<image>\nYou are a robot using the joint control. The task is \"ride the bicycle\". Please predict a possible affordance area of the end effector?",
            "from": "human"
        },
        {
            "from": "gpt",
            "value": "[0.561, 0.171, 0.645, 0.279]"
        }
    ]
},

2. Training

# Training on Stage 4_aff:
bash scripts/train/stage_4_0_resume_finetune_lora_a.sh

Note: Please change the environment variables (e.g. DATA_PATH, IMAGE_FOLDER, PREV_STAGE_CHECKPOINT) in the script to your own.

3. Convert original weights to HF weights

# Planning Model
python model/llava_utils/convert_robobrain_to_hf.py --model_dir /path/to/original/checkpoint/ --dump_path /path/to/output/
# A-LoRA & T-RoRA
python model/llava_utils/convert_lora_weights_to_hf.py --model_dir /path/to/original/checkpoint/ --dump_path /path/to/output/

⭐️ Inference

Usage for Affordance Prediction

# please refer to https://github.com/FlagOpen/RoboBrain
from inference import SimpleInference
model_id = "BAAI/RoboBrain"
lora_id = "BAAI/RoboBrain-LoRA-Affordance"
model = SimpleInference(model_id, lora_id)
# Example 1:
prompt = "You are a robot using the joint control. The task is \"pick_up the suitcase\". Please predict a possible affordance area of the end effector?"
image = "./assets/demo/affordance_1.jpg"
pred = model.inference(prompt, image, do_sample=False)
print(f"Prediction: {pred}")
'''
    Prediction: [0.733, 0.158, 0.845, 0.263]
'''
# Example 2:
prompt = "You are a robot using the joint control. The task is \"push the bicycle\". Please predict a possible affordance area of the end effector?"
image = "./assets/demo/affordance_2.jpg"
pred = model.inference(prompt, image, do_sample=False)
print(f"Prediction: {pred}")
'''
    Prediction: [0.600, 0.127, 0.692, 0.227]
'''

🤖 Evaluation

Coming Soon ...

😊 Acknowledgement

We would like to express our sincere gratitude to the developers and contributors of the following projects:

LLaVA-NeXT: The comprehensive codebase for training Vision-Language Models (VLMs).
Open-X-Emboddied: A powerful evaluation tool for Vision-Language Models (VLMs).
AGD20k: An affordance dataset that provides instructions and corresponding affordance regions.

Their outstanding contributions have played a pivotal role in advancing our research and development initiatives.

📑 Citation

If you find this project useful, welcome to cite us.

@article{ji2025robobrain,
  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}
}

BAAI
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RoboBrain-LoRA-Affordance