--- pretty_name: InternData-A1 size_categories: - n>1T task_categories: - other - robotics language: - en tags: - Embodied-AI - Robotic manipulation extra_gated_prompt: >- ### 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/). extra_gated_fields: First Name: text Last Name: text Email: text Country: country Affiliation: text Phone: text Job title: type: select options: - Student - Research Graduate - AI researcher - AI developer/engineer - Reporter - Other Research interest: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the InternData Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the InternData Privacy Policy. extra_gated_button_content: Submit --- # 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 - [🔑 Key Features](#key-features-) - [Get started 🔥](#get-started-) - [Download the Dataset](#download-the-dataset) - [Extract the Dataset](#extract-the-dataset) - [Dataset Structure](#dataset-structure) - [📅 TODO List ](#todo-list-) - [License and Citation](#license-and-citation) # 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: ```bash # 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 │ │ ├── beef_sandwich_split_aloha #task │ │ │ ├── collect_20250724 #subtask │ │ │ │ ├── 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 │ │ ├── ... ├── real │ ├── agilex_split_aloha │ │ └── ... │ ├── arx_lift2 │ │ └── ... │ ├── A2D │ │ └── ... │ ├── ... ``` This subdataset(such as `splitaloha`) was created using [LeRobot](https://github.com/huggingface/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](meta/info.json): ```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 - [x] **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](https://creativecommons.org/licenses/by-nc-sa/4.0/). Please consider citing our project if it helps your research. ```BibTeX @misc{contributors2025internroboticsrepo, title={InternData-A1}, author={InternData-A1 contributors}, howpublished={\url{https://github.com/InternRobotics/InternManip}}, year={2025} } ```