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 | Paper | GitHub
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, you can process raw RGB images from this dataset into the RoboTransfer format with geometric conditioning using the following script:
script/process_real.sh
Citation
If you use this dataset or the RoboTransfer framework in your research, please cite:
@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},
}