| 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}, | |
| } | |
| ``` |