---
license: apache-2.0
task_categories:
- robotics
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: camera_images
    list: image
  - name: depth_images
    list: image
  - name: normal_images
    list: image
  - name: frame_id
    dtype: int32
  - name: scene_id
    dtype: string
  splits:
  - name: train
    num_bytes: 3671744232.849
    num_examples: 1473
  download_size: 3336228908
  dataset_size: 3671744232.849
---

# RoboTransfer-RealData

[**Project Page**](https://horizonrobotics.github.io/robot_lab/robotransfer) | [**Paper**](https://huggingface.co/papers/2505.23171) | [**GitHub**](https://github.com/HorizonRobotics/RoboTransfer)

RoboTransfer-RealData is a real-world robotic manipulation dataset collected using the ALOHA-AgileX robot system. It was introduced as part of the paper **"RoboTransfer: Controllable Geometry-Consistent Video Diffusion for Manipulation Policy Transfer"**. 

The dataset contains real-world trajectories used to evaluate policy transfer from synthetic data generated by RoboTransfer, a diffusion-based framework designed for geometry-consistent robotic data synthesis.

## Dataset Description

The dataset includes multi-modal visual data for robotic tasks:
- `camera_images`: RGB frames captured from the robot's camera system.
- `depth_images`: Corresponding depth maps for geometric conditioning.
- `normal_images`: Estimated surface normal maps.
- `frame_id`: The sequential index of the frame.
- `scene_id`: Identifier for specific recorded scenes.

## Usage

As specified in the [RoboTransfer GitHub repository](https://github.com/HorizonRobotics/RoboTransfer), you can process raw RGB images from this dataset into the RoboTransfer format with geometric conditioning using the following script:

```bash
script/process_real.sh
```

## Citation

If you use this dataset or the RoboTransfer framework in your research, please cite:

```bibtex
@misc{liu2025robotransfergeometryconsistentvideodiffusion,
      title={RoboTransfer: Geometry-Consistent Video Diffusion for Robotic Visual Policy Transfer},
      author={Liu Liu and Xiaofeng Wang and Guosheng Zhao and Keyu Li and Wenkang Qin and Jiaxiong Qiu and Zheng Zhu and Guan Huang and Zhizhong Su},
      year={2025},
      eprint={2505.23171},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.23171},
}
```