io-intelligence/WipeTable_DualArxR5a_TeleXperience
This dataset was created using LeRobot. Dataset Description 73 real-robot teleoperation episodes for “Wipe the table.” on a DualArxR5a dual-arm robot. Format: LeRobot v3.0 (30 Hz parquet + H.264 videos). Collected with TeleXperience, IO-AI’s product for real-robot teleoperation and data collection. Task / language prompt: Wipe the table. Robot: DualArxR5a (bimanual, parallel-jaw grippers) Frames: 629523 at 30 Hz Cameras: camera_high (overhead), camera_low (lower scene)… See the full description on the dataset page: https://huggingface.co/datasets/io-intelligence/WipeTable_DualArxR5a_TeleXperience.
This dataset was created using LeRobot.
<a class="flex" href="https://huggingface.co/spaces/lerobot/visualizedataset?path=io-intelligence/WipeTableDualArxR5a_TeleXperience"> <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl.svg"/> <img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/badges/resolve/main/visualize-this-dataset-xl-dark.svg"/> </a>
Dataset Description
73 real-robot teleoperation episodes for “Wipe the table.” on a DualArxR5a dual-arm robot. Format: LeRobot v3.0 (30 Hz parquet + H.264 videos).
Collected with [TeleXperience](https://io-ai.tech/en/telexperience/), IO-AI’s product for real-robot teleoperation and data collection.
- Task / language prompt:
Wipe the table. - Robot: DualArxR5a (bimanual, parallel-jaw grippers)
- Frames: 629523 at 30 Hz
- Cameras:
camera_high(overhead),camera_low(lower scene),camera_left_wrist,camera_right_wrist - Action / state: 14-D, same names and order:
left_joint1–6,right_joint1–6,right_gripper,left_gripper(grippers in[0, 1])
- Homepage: https://io-ai.tech/en/telexperience/
- Paper: none
- License: apache-2.0
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float64",
"names": [
"left_joint1",
"left_joint2",
"left_joint3",
"left_joint4",
"left_joint5",
"left_joint6",
"right_joint1",
"right_joint2",
"right_joint3",
"right_joint4",
"right_joint5",
"right_joint6",
"right_gripper",
"left_gripper"
],
"shape": [
14
]
},
"episode_index": {
"dtype": "int64",
"shape": [
1
]
},
"frame_index": {
"dtype": "int64",
"shape": [
1
]
},
"index": {
"dtype": "int64",
"shape": [
1
]
},
"observation.images.camera_high": {
"dtype": "video",
"info": {
"has_audio": false,
"video.channels": 3,
"video.codec": "h264",
"video.fps": 30,
"video.height": 480,
"video.is_depth_map": false,
"video.pix_fmt": "yuv420p",
"video.width": 640
},
"names": [
"height",
"width",
"channels"
],
"shape": [
480,
640,
3
]
},
"observation.images.camera_left_wrist": {
"dtype": "video",
"info": {
"has_audio": false,
"video.channels": 3,
"video.codec": "h264",
"video.fps": 30,
"video.height": 480,
"video.is_depth_map": false,
"video.pix_fmt": "yuv420p",
"video.width": 640
},
"names": [
"height",
"width",
"channels"
],
"shape": [
480,
640,
3
]
},
"observation.images.camera_low": {
"dtype": "video",
"info": {
"has_audio": false,
"video.channels": 3,
"video.codec": "h264",
"video.fps": 30,
"video.height": 480,
"video.is_depth_map": false,
"video.pix_fmt": "yuv420p",
"video.width": 640
},
"names": [
"height",
"width",
"channels"
],
"shape": [
480,
640,
3
]
},
"observation.images.camera_right_wrist": {
"dtype": "video",
"info": {
"has_audio": false,
"video.channels": 3,
"video.codec": "h264",
"video.fps": 30,
"video.height": 480,
"video.is_depth_map": false,
"video.pix_fmt": "yuv420p",
"video.width": 640
},
"names": [
"height",
"width",
"channels"
],
"shape": [
480,
640,
3
]
},
"observation.state": {
"dtype": "float64",
"names": [
"left_joint1",
"left_joint2",
"left_joint3",
"left_joint4",
"left_joint5",
"left_joint6",
"right_joint1",
"right_joint2",
"right_joint3",
"right_joint4",
"right_joint5",
"right_joint6",
"right_gripper",
"left_gripper"
],
"shape": [
14
]
},
"task_index": {
"dtype": "int64",
"shape": [
1
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
]
}
},
"total_episodes": 73,
"total_frames": 629523,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"robot_type": "DualArxR5a",
"splits": {
"train": "0:73"
}
}How to load
from lerobot.datasets import LeRobotDataset
dataset = LeRobotDataset(
repo_id="io-intelligence/WipeTable_DualArxR5a_TeleXperience",
)
print(dataset)
frame = dataset[0]Local path (before upload):
dataset = LeRobotDataset(
repo_id="io-intelligence/WipeTable_DualArxR5a_TeleXperience",
root="/path/to/livingroom_wipe_table_livingroom_wipe_table_DualArxR5a",
download_videos=False,
video_backend="pyav",
)Citation
BibTeX: none