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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.
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InternData-A1
InternData-A1 is a hybrid synthetic-real manipulation dataset integrating 5 heterogeneous robots, 15 skills, and 200+ scenes, emphasizing multi-robot collaboration under dynamic scenarios.
🔑 Key Features
- Heterogeneous multi-robot platforms: ARX Lift-2, AgileX Split Aloha, Openloong Humanoid, A2D, Franka
- Hybrid synthetic-real manipulation demonstrations with task-level digital twins
- Dynamic scenarios include:
- Moving Object Manipulation in Conveyor Belt Scenarios
- Multi-robot 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
If you only want to download a specific dataset, such as splitaloha, you can use the following code.
# Make sure you have git-lfs installed (https://git-lfs.com)
git lfs install
# Initialize an empty Git repository
git init InternData-A1
cd InternData-A1
# Set the remote repository
git remote add origin https://huggingface.co/datasets/InternRobotics/InternData-A1
# Enable sparse-checkout
git sparse-checkout init
# Specify the folders and files
git sparse-checkout set physical/splitaloha
# Pull the data
git pull origin main
Extract the Dataset
After downloading the dataset, you may need to extract compressed files. Here are the method for dataset extraction:
# Navigate to the directory where the extraction script is located
cd InternData-A1/scripts
# Extract all compressed files in the current directory and subdirectories:
python A1_compress_decompress.py decompress /path/to/data
# Extract all compressed files to the new location
python A1_compress_decompress.py decompress /path/to/data -o /path/to/output
Note: Replace /path/to/data and /path/to/output with your actual data directory and desired extraction output path. Make sure you have sufficient disk space for the extracted dataset.
Dataset Structure
Folder hierarchy
data
├── simulated
│ ├── agilex_split_aloha
│ │ ├── 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
│ │ │ │ ├── ...
│ │ │ ├── ...
│ ├── arx_lift2
│ ├── ...
├── physical
│ ├── agilex_split_aloha
│ │ └── ...
│ └── arx_lift2
│ │ └── ...
│ └── A2D
│ │ └── ...
│ ├── ...
This subdataset(such as splitaloha) 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
- InternData-A1: ~200,000 simulation demonstrations and ~10,000 real-world robot demonstrations
- ~1,000,000 trajectories of hybrid synthetic-real robotic manipulation 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}
}
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