Datasets:
metadata
pretty_name: InternData-A1
size_categories:
- n>1T
task_categories:
- other
- robotics
language:
- en
tags:
- Embodied-AI
- Robotic manipulation
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### InternData-A1 COMMUNITY LICENSE AGREEMENT
InternData-A1 Release Date: July 26, 2025. All the data and code within this
repo are under [CC BY-NC-SA
4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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InternData-A1
InternData-A1 is a hybrid synthetic-real manipulation dataset containing over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation.
🔑 Key Features
- Heterogeneous multi-robot platforms: ARX Lift-2, AgileX Split Aloha, A2D, Franka
- Hybrid synthetic-real manipulation demonstrations with task-level digital twins, containing four task categories:
- Articulation tasks
- Basic tasks
- Long-horizon tasks
- Pick and place tasks
- Diverse scenarios include:
- Moving Object Manipulation in Conveyor Belt Scenarios
- Rigid, articulated, deformable, and fluid-object manipulation
- Multi-robot / multi-arm collaboration
- Human-robot interaction
📋 Table of Contents
Get started 🔥
Download the Dataset
To download the full dataset, you can use the following code. If you encounter any issues, please refer to the official Hugging Face documentation.
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
# When prompted for a password, use an access token with write permissions.
# Generate one from your settings: https://huggingface.co/settings/tokens
git clone https://huggingface.co/datasets/InternRobotics/InternData-A1
# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/InternRobotics/InternData-A1
Dataset Structure
Folder hierarchy
data
├── sim
│ ├── articulation_tasks
│ │ └── ...
│ ├── basic_tasks
│ │ └── ...
│ ├── long_horizon_tasks # category
│ │ ├── franka # robot
│ │ │ └── ...
│ │ ├── lift2
│ │ │ ├── sort_the_rubbish # task
│ │ │ │ ├── data
│ │ │ │ │ ├── chunk-000
│ │ │ │ │ │ ├── episode_000000.parquet
│ │ │ │ │ │ ├── episode_000001.parquet
│ │ │ │ │ │ ├── episode_000002.parquet
│ │ │ │ │ │ ├── ...
│ │ │ │ │ ├── chunk-001
│ │ │ │ │ │ ├── ...
│ │ │ │ │ ├── ...
│ │ │ │ ├── meta
│ │ │ │ │ ├── episodes.jsonl
│ │ │ │ │ ├── episodes_stats.jsonl
│ │ │ │ │ ├── info.json
│ │ │ │ │ ├── modality.json
│ │ │ │ │ ├── stats.json
│ │ │ │ │ ├── tasks.jsonl
│ │ │ │ ├── videos
│ │ │ │ │ ├── chunk-000
│ │ │ │ │ │ ├── images.rgb.head
│ │ │ │ │ │ │ ├── episode_000000.mp4
│ │ │ │ │ │ │ ├── episode_000001.mp4
│ │ │ │ │ │ │ ├── ...
│ │ │ │ │ │ ├── ...
│ │ │ │ │ ├── chunk-001
│ │ │ │ │ │ ├── ...
│ │ │ │ │ ├── ...
│ │ │ ├──...
│ │ ├── split_aloha
│ │ │ └── ...
│ │ ├── ...
│ ├── pick_and_place_tasks
│ │ └── ...
│ ├── ...
├── real
│ ├── ...
This subdataset(such as sort_the_rubbish) was created using LeRobot (dataset v2.1). For GROOT training framework compatibility, additional stats.json and modality.json files are included, where stats.json provides statistical values (mean, std, min, max, q01, q99) for each feature across the dataset, and modality.json defines model-related custom modalities.
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "piper",
"total_episodes": 100,
"total_frames": 49570,
"total_tasks": 1,
"total_videos": 300,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:100"
},
"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": {
"images.rgb.head": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 30.0,
"video.height": 720,
"video.width": 1280,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"images.rgb.hand_left": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"images.rgb.hand_right": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 30.0,
"video.height": 480,
"video.width": 640,
"video.channels": 3,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"states.left_joint.position": {
"dtype": "float32",
"shape": [
6
],
"names": [
"left_joint_0",
"left_joint_1",
"left_joint_2",
"left_joint_3",
"left_joint_4",
"left_joint_5"
]
},
"states.left_gripper.position": {
"dtype": "float32",
"shape": [
1
],
"names": [
"left_gripper_0"
]
},
"states.right_joint.position": {
"dtype": "float32",
"shape": [
6
],
"names": [
"right_joint_0",
"right_joint_1",
"right_joint_2",
"right_joint_3",
"right_joint_4",
"right_joint_5"
]
},
"states.right_gripper.position": {
"dtype": "float32",
"shape": [
1
],
"names": [
"right_gripper_0"
]
},
"actions.left_joint.position": {
"dtype": "float32",
"shape": [
6
],
"names": [
"left_joint_0",
"left_joint_1",
"left_joint_2",
"left_joint_3",
"left_joint_4",
"left_joint_5"
]
},
"actions.left_gripper.position": {
"dtype": "float32",
"shape": [
1
],
"names": [
"left_gripper_0"
]
},
"actions.right_joint.position": {
"dtype": "float32",
"shape": [
6
],
"names": [
"right_joint_0",
"right_joint_1",
"right_joint_2",
"right_joint_3",
"right_joint_4",
"right_joint_5"
]
},
"actions.right_gripper.position": {
"dtype": "float32",
"shape": [
1
],
"names": [
"right_gripper_0"
]
},
"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
}
}
}
key format in features
Select appropriate keys for features based on characteristics such as ontology, single-arm or bimanual-arm, etc.
|-- images
|-- rgb
|-- head
|-- hand_left
|-- hand_right
|-- states
|-- left_joint
|-- position
|-- right_joint
|-- position
|-- left_gripper
|-- position
|-- right_gripper
|-- position
|-- actions
|-- left_joint
|-- position
|-- right_joint
|-- position
|-- left_gripper
|-- position
|-- right_gripper
|-- position
📅 TODO List
- Released: 632k simulation pretraining data (over 7433 hours).
- To be released: real-world post-training data.
License and Citation
All the data and code within this repo are under CC BY-NC-SA 4.0. Please consider citing our project if it helps your research.
@misc{contributors2025internroboticsrepo,
title={InternData-A1},
author={InternData-A1 contributors},
howpublished={\url{https://github.com/InternRobotics/InternManip}}, year={2025}
}