Datasets:
File size: 9,299 Bytes
f3fa3c5 f5dffce f3fa3c5 dd56411 fcf620c dd56411 766e7ad df06083 dd56411 df06083 dd56411 df06083 dd56411 df06083 dd56411 df06083 dd56411 df06083 dd56411 df06083 dd56411 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 | ---
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
*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
}
}
}
```
|