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metadata
license: apache-2.0
task_categories:
  - robotics
tags:
  - LeRobot
  - TeleXperience
  - DualArxR5a
  - teleoperation
  - dual-arm
  - wipe-table
  - living-room
configs:
  - config_name: default
    data_files: data/*/*.parquet

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), 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