# Cloth-Folding Dataset for X-VLA Paper This dataset contains 1,500 episodes of cloth folding, collected using Agilex's robotic arm. It was used in the **X-VLA** paper for cloth-folding tasks, showcasing a near-perfect success rate in folding accuracy. # Dataset Overview - Total Episodes: ～1,500 - Task: Automated cloth folding - Robot: Agilex Aloha - Performance: Near 100% success rate in completing the folding task # Hardware setup We observed that the camera setup of the official Agilex Aloha platform is positioned relatively low, which prevents it from capturing the full cloth-folding process, where many frames fail to include the robot arms. To address this issue, we modified the camera setup accordingly. You can find the \`.stl\`, \`.step\`, \`.sldprt\` files of our new camera mount, which can be used for 3D printing. The installation instruction can be found in the \`camera_mount_install.md\`. # Usage You can find \`.hdf5\` and \`.mp4\` files in each directory. The \`.mp4\` files are just used for visulization and are not used for training. The \`.hdf5\` files contains all necessary keys and data, including: ## HDF5 file hierarchy \`\`\` ├── action # nx14 absolute bimanual joints, not used in our paper ├── base_action # nx2 chassis actions, not used in our paper ├── language_instruction # "fold the cloth" ├── observations │ ├── eef # nx14 absolute eef pos using euler angles to represent the rotation, not used in our paper │ │ eef_quaternion # nx16 absolute eef pos using quaternion to represent the rotation, not used in our paper │ │ eef_6d # nx20 absolute eef pos using rotate6d to represent the rotation │ │ eef_left_time # nx1 the time stamp for left arm eef pos, can be used for resample or interpolation │ │ eef_right_time # nx1 the time stamp for right arm eef pos, can be used for resample or interpolation │ ├── qpos # nx14 absolute bimanual joints, not used in our paper │ ├── qpos_left_time # nx1 the time stamp for left arm joint pos, can be used for resample or interpolation, not used in our paper │ ├── qpos_right_time # nx1 the time stamp for right arm joint pos, can be used for resample or interpolation, not used in our paper │ ├── qvel # nx14 bimanual joint velocity, not used in our paper │ ├── effort # nx14 bimanual joint effort, not used in our paper │ ├── images │ │ ├── cam_high # the encoded head cam view, should be decoded using cv2 │ │ ├── cam_left_wrist # the encoded left wrist view, should be decoded using cv2 │ │ ├── cam_right_wrist # the encoded right wrist view, should be decoded using cv2 ├── time_stamp # the time stamp for each sample, not used in our paper \`\`\` How to read the hdf5 file: \`\`\` import h5py import cv2 import io from mmengine import fileio path = "REPLACE TO YOUR HDF5 FILE PATH HERE" # load the hdf5 file value = fileio.get(path) f = io.BytesIO(value) h = h5py.File(f,'r') # you can monitor the hdf5 hierarchy by print out its keys print(h.keys()) # this is one example to read out the data, for example, the 'cam_high' data head_view_bytes = h['observations/images/cam_high'][()] # NOTE: we compress all images to bytes using cv2.imencode head_view = cv2.imdecode(head_view_bytes, cv2.IMREAD_COLOR) # NOTE: we should decode it back to RGB images for further usage #Then you can go free to use our data :) # ... # ... \`\`\` ## Visualize the data You can find some dictionary have \`.mp4\` file for visulization. If you want to visualize all the \`.hdf5\` file, you can run the following code: \`\`\` from mmengine import fileio import io import h5py import cv2 import matplotlib.pyplot as plt from tqdm import tqdm from PIL import Image from IPython.display import display, Image as IPImage from IPython.display import Video import os import imageio import numpy as np # Just replace the path here, then run this script. This script will generate all the .mp4 files for the .hdf5 file top_path = 'REPLACE TO YOUR XVLA-SOFT-FOLD PATH' hdf5_files = fileio.list_dir_or_file(top_path, suffix='.hdf5', recursive=True, list_dir=False) for hdf5_name in hdf5_files: path = os.path.join(top_path, hdf5_name) # Prepare OpenCV VideoWriter to save as MP4 video_path = path.replace('.hdf5', '.mp4') fps = 30 # Adjust the FPS if needed image_list = [] print(video_path) if os.path.exists(video_path): print(f"pass \{video_path\}, it already exists") continue value = fileio.get(path) f = io.BytesIO(value) h = h5py.File(f,'r') images = h['/observations/images/cam_high'][()] images_left = h['/observations/images/cam_left_wrist'][()] images_right = h['/observations/images/cam_right_wrist'][()] ep_len = images.shape[0] for i in tqdm(range(ep_len)): img = images[i] img_left = images_left[i] img_right = images_right[i] img = cv2.imdecode(img, cv2.IMREAD_COLOR) # Decode image from bytes img_left = cv2.imdecode(img_left, cv2.IMREAD_COLOR) # Decode image from bytes img_right = cv2.imdecode(img_right, cv2.IMREAD_COLOR) # Decode image from bytes img = np.concatenate([img, img_left, img_right], axis = 1) image_list.append(img) # Release the VideoWriter and show output imageio.mimsave(video_path, image_list, fps=fps) \`\`\` # Citation If you use this dataset in your research or for any related work, please cite the X-VLA Paper: \`\`\` @article\{zheng2025x, title=\{X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model\}, author=\{Zheng, Jinliang and Li, Jianxiong and Wang, Zhihao and Liu, Dongxiu and Kang, Xirui and Feng, Yuchun and Zheng, Yinan and Zou, Jiayin and Chen, Yilun and Zeng, Jia and others\}, journal=\{arXiv preprint arXiv:2510.10274\}, year=\{2025\} \} \`\`\`