Data Formats
The IO Data Platform supports flexible data formats and allows for custom data visualization templates.
Below is an example using data collected by the IO data acquisition product:
Human Data Format
File Structure
f"{date}_{project}_{scene}_{task}_{staff_id}_{timestamp}"
├── align_result.csv # Timestamp alignment table
├── annotation.json # Annotation data
├── config # Camera and sensor configuration
│ ├── calib_data.yml
│ ├── depth_to_rgb.yml
│ ├── mocap_main.yml
│ ├── orbbec_depth.yml
│ ├── orbbec_rgb.yml
│ └── pose_calib.yml
└── data.mcap # Multimodal data
Multimodal Data
library: mcap go v1.7.0
profile: ros1
messages: 45200
duration: 1m5.625866496s
start: 2025-01-15T18:09:29.628202496+08:00 (1736935769.628202496)
end: 2025-01-15T18:10:35.254068992+08:00 (1736935835.254068992)
compression:
zstd: [764/764 chunks] [6.13 GiB/3.84 GiB (37.39%)] [59.87 MiB/sec]
channels:
(1) /rgbd/color/image_raw/compressed 1970 msgs (30.02 Hz) : sensor_msgs/CompressedImage [ros1msg]
(2) /joint_states 1970 msgs (30.02 Hz) : sensor_msgs/JointState [ros1msg]
(3) /claws_r_hand 1970 msgs (30.02 Hz) : io_msgs/claws_angle [ros1msg]
(4) /global_localization 1970 msgs (30.02 Hz) : geometry_msgs/PoseStamped [ros1msg]
(5) /robot_description 1 msgs : std_msgs/String [ros1msg]
(6) /ee_visualization 1970 msgs (30.02 Hz) : sensor_msgs/CompressedImage [ros1msg]
(7) /rgbd/depth/image_raw 1970 msgs (30.02 Hz) : sensor_msgs/Image [ros1msg]
(8) /colorized_depth 1970 msgs (30.02 Hz) : sensor_msgs/CompressedImage [ros1msg]
(9) /claws_l_hand 1970 msgs (30.02 Hz) : io_msgs/claws_angle [ros1msg]
(10) /claws_touch_data 1970 msgs (30.02 Hz) : io_msgs/squashed_touch [ros1msg]
(11) /touch_visualization 1970 msgs (30.02 Hz) : sensor_msgs/CompressedImage [ros1msg]
(12) /mocap/sensor_data 1970 msgs (30.02 Hz) : io_msgs/squashed_mocap_data [ros1msg]
(13) /mocap/ros_tf 1970 msgs (30.02 Hz) : tf2_msgs/TFMessage [ros1msg]
(14) /left_ee_pose 1970 msgs (30.02 Hz) : geometry_msgs/PoseStamped [ros1msg]
(15) /right_ee_pose 1970 msgs (30.02 Hz) : geometry_msgs/PoseStamped [ros1msg]
(16) /usb_cam_left/mjpeg_raw/compressed 1960 msgs (29.87 Hz) : sensor_msgs/CompressedImage [ros1msg]
(17) /usb_cam_right/mjpeg_raw/compressed 1946 msgs (29.65 Hz) : sensor_msgs/CompressedImage [ros1msg]
(18) /usb_cam_fisheye/mjpeg_raw/compressed 1957 msgs (29.82 Hz) : sensor_msgs/CompressedImage [ros1msg]
(19) /realsense_left_hand/depth/image_rect_raw 1961 msgs (29.88 Hz) : sensor_msgs/Image [ros1msg]
(20) /realsense_left_hand/color/image_raw/compressed 1961 msgs (29.88 Hz) : sensor_msgs/CompressedImage [ros1msg]
(21) /realsense_right_hand/depth/image_rect_raw 1947 msgs (29.67 Hz) : sensor_msgs/Image [ros1msg]
(22) /realsense_right_hand/color/image_raw/compressed 1947 msgs (29.67 Hz) : sensor_msgs/CompressedImage [ros1msg]
(23) /world_left_ee_pose 1970 msgs (30.02 Hz) : geometry_msgs/PoseStamped [ros1msg]
(24) /world_right_ee_pose 1970 msgs (30.02 Hz) : geometry_msgs/PoseStamped [ros1msg]
channels: 24
attachments: 0
metadata: 0
| Topic Name | Description |
|---|---|
| /mocap/sensor_data | Joint velocity, acceleration, angular velocity, rotation angle, and sensor data from motion capture |
| /mocap/ros_tf | TF of all joints from motion capture |
| /joint_states | JointState of all joints from motion capture |
| /right_ee_pose | Right gripper pose in the main head camera coordinate system |
| /left_ee_pose | Left gripper pose in the main head camera coordinate system |
| /claws_l_hand | Left gripper closure degree |
| /claws_r_hand | Right gripper closure degree |
| /claws_touch_data | Gripper tactile data (contains two messages, each message's frame_id indicates left or right gripper, first four values of data are valid) |
| /realsense_left_hand/color/image_raw/compressed | RGB image from left gripper camera |
| /realsense_left_hand/depth/image_rect_raw | Depth image from left gripper camera |
| /realsense_right_hand/color/image_raw/compressed | RGB image from right gripper camera |
| /realsense_right_hand/depth/image_rect_raw | Depth image from right gripper camera |
| /rgbd/color/image_raw/compressed | RGB image from main head camera |
| /rgbd/depth/image_raw | Depth image from main head camera |
| /colorized_depth | Colorized depth image from main head camera |
| /usb_cam_fisheye/mjpeg_raw/compressed | RGB image from main head fisheye camera |
| /usb_cam_left/mjpeg_raw/compressed | RGB image from main head left monocular camera |
| /usb_cam_right/mjpeg_raw/compressed | RGB image from main head right monocular camera |
| /ee_visualization | End-effector pose visualization in main head camera RGB image |
| /touch_visualization | Gripper tactile data visualization |
| /robot_description | Motion capture URDF |
| /global_localization | Main head camera pose in world coordinate system |
| /world_left_ee_pose | Left gripper pose in world coordinate system |
| /world_right_ee_pose | Right gripper pose in world coordinate system |
If the data is collected with a person wearing tactile gloves, an additional tactile digital signal array topic is included:
/mocap/touch_data 57 msgs (30.25 Hz): io_msgs/squashed_touc [ros1msg]
Natural Language Annotation Data
{
"belong_to": "20250115_InnerTest_PublicArea_TableClearing_szk_180926",
"mocap_offset": [],
"object_set": [
"paper cup",
"placemat",
"trash can",
"napkin",
"plate",
"dinner knife",
"tableware storage box",
"wine glass",
"dinner fork"
],
"scene": "PublicArea",
"skill_set": [
"pick {A} from {B}",
"toss {A} into {B}",
"place {A} on {B}"
],
"subtasks": [
{
"skill": "pick {A} from {B}",
"description": "pick the paper cup from the placemat with the left gripper",
"description_zh": "左夹爪 从 餐垫 捡起 纸杯",
"end_frame_id": 227,
"end_timestamp": "1736935777206000000",
"sequence_id": 1,
"start_frame_id": 159,
"start_timestamp": "1736935774906000000",
"comment": "",
"attempts": "success"
},
{
"skill": "toss {A} into {B}",
"description": "toss the paper cup into the trash can with the left gripper",
"description_zh": "左夹爪 扔纸杯进垃圾桶",
"end_frame_id": 318,
"end_timestamp": "1736935780244000000",
"sequence_id": 2,
"start_frame_id": 231,
"start_timestamp": "1736935777306000000",
"comment": "",
"attempts": "success"
},
...
],
"tag_set": [],
"task_description": "20250115_InnerTest_PublicArea_TableClearing_szk_180926"
}
Teleoperation Robot Data Format
File Structure
f"{robot_name}_{date}_{timestamp}_{sequence_id}"
├── RM_AIDAL_250124_172033_0.mcap
├── RM_AIDAL_250124_172033_0.json
├── RM_AIDAL_250126_093648_0.metadata.yaml
Multimodal Data
Files: RM_AIDAL_250126_091041_0.mcap
Bag size: 443.3 MiB
Storage id: mcap
Duration: 100.052164792s
Start: Jan 24 2025 21:37:32.526605552 (1737725852.526605552)
End: Jan 24 2025 21:39:12.578770344 (1737725952.578770344)
Messages: 62116
Topic information: Topic: /camera_01/color/image_raw/compressed | Type: sensor_msgs/msg/CompressedImage | Count: 3000 | Serialization Format: cdr
Topic: /camera_02/color/image_raw/compressed | Type: sensor_msgs/msg/CompressedImage | Count: 3000 | Serialization Format: cdr
Topic: /camera_03/color/image_raw/compressed | Type: sensor_msgs/msg/CompressedImage | Count: 3000 | Serialization Format: cdr
Topic: io_teleop/joint_states | Type: sensor_msgs/msg/JointState | Count: 1529 | Serialization Format: cdr
Topic: io_teleop/joint_cmd | Type: sensor_msgs/msg/JointState | Count: 10009 | Serialization Format: cdr
Topic: io_teleop/target_ee_poses | Type: geometry_msgs/msg/PoseArray | Count: 10014 | Serialization Format: cdr
Topic: io_teleop/target_base_move | Type: std_msgs/msg/Float64MultiArray | Count: 10010 | Serialization Format: cdr
Topic: io_teleop/target_gripper_status | Type: sensor_msgs/msg/JointState | Count: 10012 | Serialization Format: cdr
Topic: io_teleop/target_joint_from_vr | Type: sensor_msgs/msg/JointState | Count: 10012 | Serialization Format: cdr
Topic: /robot_description | Type: std_msgs/msg/String | Count: 1 | Serialization Format: cdr
Topic: /tf | Type: tf2_msgs/msg/TFMessage | Count: 1529 | Serialization Format: cdr
| Topic Name | Description |
|---|---|
| /camera_01/color/image_raw/compressed | RGB image from main camera |
| /camera_02/color/image_raw/compressed | RGB image from left camera |
| /camera_03/color/image_raw/compressed | RGB image from right camera |
| io_teleop/joint_states | Joint states |
| io_teleop/joint_cmd | Joint commands |
| io_teleop/target_ee_poses | Target end-effector poses |
| io_teleop/target_base_move | Target base movement |
| io_teleop/target_gripper_status | Target gripper status |
| io_teleop/target_joint_from_vr | Joint targets from VR device |
| /robot_description | Robot URDF description |
| /tf | TF spatial pose transformation information |
Natural Language Annotation Data
{
"belong_to": "RM_AIDAL_250126_091041_0",
"mocap_offset": [],
"object_set": [
"lemon candy",
"plate",
"pistachios"
],
"scene": "250126",
"skill_set": [
"place {A} on {B}"
],
"subtasks": [
{
"skill": "place {A} on {B}",
"objecta": "lemon candy",
"objectb": "plate",
"options": [
"leftHand"
],
"description": "place the lemon candy on the plate with the left hand",
"end_timestamp": "1737725886915000000",
"sequence_id": 1,
"start_timestamp": "1737725880757000000",
"comment": "",
"attempts": "success"
},
{
"skill": "place {A} on {B}",
"objecta": "pistachios",
"objectb": "plate",
"options": [
"rightHand"
],
"description": "place the pistachios on the plate with the right hand",
"end_timestamp": "1737725950745000000",
"sequence_id": 2,
"start_timestamp": "1737725941657000000",
"comment": "",
"attempts": "success"
}
],
"tag_set": [],
"task_description": "20250205_RM_ItemPacking_zhouxw"
}
Model Training Data
We provide tools to convert the above mcap and json data into Python-parsable formats for direct use in large model training.
HDF5 Format
Below is a basic data example. The actual training data format may vary depending on the original data and customer customization requirements:
/root
├── metadata (Group)
│ ├── creation_time (Attribute)
│ ├── source (Attribute)
│ ├── schema (Dataset)
│
├── messages (Group)
│ ├── /camera_01/color/image_raw/compressed (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── /camera_02/color/image_raw/compressed (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── /camera_03/color/image_raw/compressed (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/joint_states (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/joint_cmd (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/target_ee_poses (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/target_base_move (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/target_gripper_status (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── io_teleop/target_joint_from_vr (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── /robot_description (Group)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
│ │
│ ├── /tf (Group)
│ │ ├── timestamps (Dataset)
│ │ ├── data (Dataset)
│ │ ├── schema_id (Attribute)
LeRobot Format
You can refer to our sample dataset: https://huggingface.co/datasets/io-ai-data/DesktopCleanup_RM_AIDAL_demo
{
"codebase_version": "v2.1",
"robot_type": "custom_arm",
"total_episodes": 20,
"total_frames": 5134,
"total_tasks": 20,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"observation.images.camera_01": {
"dtype": "image",
"shape": [
480,
640,
3
]
},
"observation.images.camera_02": {
"dtype": "image",
"shape": [
480,
640,
3
]
},
"observation.images.camera_03": {
"dtype": "image",
"shape": [
480,
640,
3
]
},
"observation.images.camera_04": {
"dtype": "image",
"shape": [
480,
640,
3
]
},
"observation.state": {
"dtype": "float64",
"shape": [
37
],
"names": [
"r_joint1",
"r_joint2",
"r_joint3",
"r_joint4",
"r_joint5",
"r_joint6",
"l_joint1",
"l_joint2",
"l_joint3",
"l_joint4",
"l_joint5",
"l_joint6",
"R_thumb_MCP_joint1",
"R_thumb_MCP_joint2",
"R_thumb_PIP_joint",
"R_thumb_DIP_joint",
"R_index_MCP_joint",
"R_index_DIP_joint",
"R_middle_MCP_joint",
"R_middle_DIP_joint",
"R_ring_MCP_joint",
"R_ring_DIP_joint",
"R_pinky_MCP_joint",
"R_pinky_DIP_joint",
"L_thumb_MCP_joint1",
"L_thumb_MCP_joint2",
"L_thumb_PIP_joint",
"L_thumb_DIP_joint",
"L_index_MCP_joint",
"L_index_DIP_joint",
"L_middle_MCP_joint",
"L_middle_DIP_joint",
"L_ring_MCP_joint",
"L_ring_DIP_joint",
"L_pinky_MCP_joint",
"L_pinky_DIP_joint",
"platform_joint"
]
},
"action": {
"dtype": "float64",
"shape": [
12
],
"names": [
"l_joint1",
"l_joint2",
"l_joint3",
"l_joint4",
"l_joint5",
"l_joint6",
"r_joint1",
"r_joint2",
"r_joint3",
"r_joint4",
"r_joint5",
"r_joint6"
]
},
"observation.gripper": {
"dtype": "float64",
"shape": [
2
],
"names": [
"right_gripper",
"left_gripper"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
}
}