| --- |
| license: mit |
| pretty_name: SMART-Data |
| tags: |
| - robotics |
| - manipulation |
| - embodied-ai |
| - lerobot |
| - simulation |
| --- |
| |
| # SMART-Data |
|
|
|  |
|
|
| <p align="center"> |
| <a href="https://teamillusion-smart.github.io/"><img alt="Project: teamillusion-smart.github.io" src="https://img.shields.io/badge/Project-teamillusion--smart.github.io-2563eb?logo=github&logoColor=white&style=flat-square"></a> <a href="https://arxiv.org/abs/2610.07652"><img alt="arXiv: 2610.07652" src="https://img.shields.io/badge/arXiv-2610.07652-b31b1b?logo=arxiv&logoColor=white&style=flat-square"></a> |
| </p> |
|
|
| **SMART-Data** is a large-scale synthetic dataset for articulated-object manipulation, generated entirely in simulation with a scalable synthesis pipeline.<br> |
| It covers five robot setups and a hierarchical taxonomy of atomic, composite, and long-horizon tasks, spanning 23 manipulation skills, 44 task types, and diverse articulated objects with revolute, prismatic, and compound joints.<br> |
| The released demonstrations are organized in LeRobot v3.0 format for robot learning and VLA pretraining. |
|
|
| ## 🔭 Dataset Overview |
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|  |
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| *Statistics of SMART-Data, including robot-setup distribution, task complexity, and articulated-object coverage.* |
|
|
| ## ✨ Dataset Features |
|
|
| - **Large-scale synthetic data:** SMART-Data contains over 1M simulated manipulation episodes, approximately 500M frames, and over 4,600 hours of demonstrations. |
| - **Five robot setups:** The dataset covers ARX AC One, R1Pro, Dual RM75, Dual Franka, and Single Franka, providing demonstrations from both single-arm and dual-arm embodiments. |
| - **Hierarchical task design:** Tasks are organized into atomic, composite, and long-horizon categories, supporting primitive skills as well as multi-stage compositional reasoning. |
| - **Broad skill coverage:** The dataset spans 23 manipulation skills and 44 task types across approximately 10K task YAML configurations. |
| - **Diverse articulated objects:** It covers 23 of the 88 functional object categories in the SMART-Sim asset library, including 2,507 articulated objects with revolute, prismatic, and compound joints. |
| - **Rich interaction diversity:** Demonstrations vary in object pose, robot initialization, camera extrinsics, visual appearance, and physical properties. |
| - **Multi-view observations:** The data include global and arm-specific camera views for learning manipulation policies from complementary perspectives. |
| - **Scalable scene coverage:** The released trajectories are collected across 1,122 distinct scenes. |
| - **VLA-ready format:** All demonstrations are stored in LeRobot v3.0 format for behavior cloning, VLA pretraining, and downstream manipulation research. |
|
|
| ## 🏁 Get Started |
|
|
| ### ⬇️ Download |
|
|
| ```bash |
| # Make sure you have Git LFS installed |
| git lfs install |
| |
| # For private repositories, use a Hugging Face access token with read access |
| git clone https://huggingface.co/datasets/TeleEmbodied/SMART-Data |
| |
| # Clone without downloading the large files, leaving only their pointers |
| GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/TeleEmbodied/SMART-Data |
| ``` |
|
|
| ### 🗂️ Dataset Structure |
|
|
| The following example shows the fully expanded dataset layout: |
|
|
| ```text |
| SMART-Data/ |
| ├── AC1/ # robot platform |
| │ ├── atomic/ # primitive tasks |
| │ │ ├── left/ |
| │ │ │ └── left_close_dishwasher/ # task |
| │ │ │ ├── data/ |
| │ │ │ │ └── chunk-000/ |
| │ │ │ │ ├── file-000.parquet |
| │ │ │ │ ├── file-001.parquet |
| │ │ │ │ ├── file-002.parquet |
| │ │ │ │ └── ... |
| │ │ │ ├── meta/ |
| │ │ │ │ ├── episodes/ |
| │ │ │ │ │ └── chunk-000/ |
| │ │ │ │ │ ├── file-000.parquet |
| │ │ │ │ │ ├── file-001.parquet |
| │ │ │ │ │ ├── file-002.parquet |
| │ │ │ │ │ └── ... |
| │ │ │ │ ├── info.json |
| │ │ │ │ ├── norm_stats.json |
| │ │ │ │ ├── stats.json |
| │ │ │ │ ├── tasks.parquet |
| │ │ │ │ └── ... |
| │ │ │ ├── videos/ |
| │ │ │ │ ├── observation.images.camera_global/ |
| │ │ │ │ │ └── chunk-000/ |
| │ │ │ │ │ ├── file-000.mp4 |
| │ │ │ │ │ ├── file-001.mp4 |
| │ │ │ │ │ └── ... |
| │ │ │ │ ├── observation.images.camera_left/ |
| │ │ │ │ │ └── ... |
| │ │ │ │ ├── observation.images.camera_right/ |
| │ │ │ │ │ └── ... |
| │ │ │ │ └── ... |
| │ │ │ └── ... |
| │ │ ├── right/ |
| │ │ │ └── ... |
| │ │ └── ... |
| │ ├── composite/ # multi-skill tasks |
| │ │ └── ... |
| │ └── long_horizon/ # long-horizon tasks |
| │ └── ... |
| ├── Franka_dual/ |
| │ └── ... |
| ├── Franka_single/ |
| │ └── ... |
| ├── R1Pro/ |
| │ └── ... |
| └── RM75/ |
| └── ... |
| ``` |
|
|
| ### 📄 Data Format |
|
|
| ```text |
| <task>/ # LeRobot dataset |
| ├── data/ # frame-level data |
| │ └── chunk-000/ |
| │ └── file-000.parquet # action, state, and timestamps |
| ├── meta/ # dataset metadata |
| │ ├── info.json # schema and dataset information |
| │ ├── norm_stats.json |
| │ ├── stats.json |
| │ ├── tasks.parquet # task instruction |
| │ └── episodes/ # episode metadata |
| └── videos/ # camera streams |
| ├── observation.images.camera_global/ |
| ├── observation.images.camera_left/ |
| └── observation.images.camera_right/ |
| ``` |
|
|
| #### 🔎 Example `meta/info.json` |
|
|
| ```json |
| { |
| "codebase_version": "v3.0", |
| "robot_type": "AC1", |
| "total_episodes": 500, |
| "total_frames": 170756, |
| "total_tasks": 1, |
| "chunks_size": 1000, |
| "data_files_size_in_mb": 100, |
| "video_files_size_in_mb": 200, |
| "fps": 30, |
| "splits": { |
| "train": "0:500" |
| }, |
| "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", |
| "features": { |
| "observation.images.camera_global": { |
| "dtype": "video", |
| "shape": [540, 960, 3], |
| "names": ["height", "width", "rgb"], |
| "info": { |
| "video.height": 540, |
| "video.width": 960, |
| "video.codec": "h264", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 30, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.images.camera_left": { |
| "dtype": "video", |
| "shape": [360, 640, 3], |
| "names": ["height", "width", "rgb"], |
| "info": { |
| "video.height": 360, |
| "video.width": 640, |
| "video.codec": "h264", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 30, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "observation.images.camera_right": { |
| "dtype": "video", |
| "shape": [360, 640, 3], |
| "names": ["height", "width", "rgb"], |
| "info": { |
| "video.height": 360, |
| "video.width": 640, |
| "video.codec": "h264", |
| "video.pix_fmt": "yuv420p", |
| "video.is_depth_map": false, |
| "video.fps": 30, |
| "video.channels": 3, |
| "has_audio": false |
| } |
| }, |
| "action": { |
| "dtype": "float32", |
| "shape": [14], |
| "names": { |
| "motors": [ |
| "left_arm_0", "left_arm_1", "left_arm_2", |
| "left_arm_3", "left_arm_4", "left_arm_5", |
| "right_arm_0", "right_arm_1", "right_arm_2", |
| "right_arm_3", "right_arm_4", "right_arm_5", |
| "left_gripper", "right_gripper" |
| ] |
| } |
| }, |
| "observation.state": { |
| "dtype": "float32", |
| "shape": [14], |
| "names": [ |
| "left_arm_0", "left_arm_1", "left_arm_2", |
| "left_arm_3", "left_arm_4", "left_arm_5", |
| "right_arm_0", "right_arm_1", "right_arm_2", |
| "right_arm_3", "right_arm_4", "right_arm_5", |
| "left_gripper", "right_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 |
| } |
| } |
| } |
| ``` |
|
|
