---
pretty_name: AgiBot World
size_categories:
- n>1T
task_categories:
- other
language:
- en
tags:
- real-world
- dual-arm
- Robotics manipulation
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### AgiBot World COMMUNITY LICENSE AGREEMENT
AgiBot World Alpha Release Date: December 30, 2024 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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The information you provide will be collected, stored, processed and shared in
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---
# Key Features 🔑
- **1 million+** steps of enhanced digital twins of long-horizon real-world tasks from Agibot World.
- **500,000+** steps of atomic tasks automatically generated by agents.
- **180+ classes of objects**.
- **5 classes of scenes**.
# News 🌍
- **`[2025/2/24]`** AgiBot Digital World released on Huggingface. [Download Link](https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld/tree/main)
# TODO List 📅
- [ ] **AgiBot Digital World**: More high-quality simulation data, including atomic skill task data and digital twin-enhanced data aligned with tasks in Agibot World. (ongoing open source)
# Table of Contents
- [Key Features 🔑](#key-features-)
- [News 🌍](#news-)
- [TODO List 📅](#todo-list-)
- [Get started 🔥](#get-started-)
- [Download the Dataset](#download-the-dataset)
- [Dataset Structure](#dataset-structure)
- [Explanation of Proprioceptive State](#explanation-of-proprioceptive-state)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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/agibot-world/AgiBotDigitalWorld
# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld
```
If you only want to download a specific task from the AgiBotDigitalWorld dataset, such as `digitaltwin_5`,follow these steps:.
```
# Ensure Git LFS is installed (https://git-lfs.com)
git lfs install
# Initialize an empty Git repository
git init AgiBotDigitalWorld
cd AgiBotDigitalWorld
# Set the remote repository
git remote add origin https://huggingface.co/datasets/agibot-world/AgiBotDigitalWorld
# Enable sparse-checkout
git sparse-checkout init
# Specify the folders and files you want to download
git sparse-checkout set observations/digitaltwin_5 task_info/digitaltwin_5.json scripts proprio_stats parameters
# Pull the data from the main branch
git pull origin main
```
To facilitate the inspection of the dataset's internal structure and examples, we also provide a sample dataset. Please refer to `sample_dataset.zip`.
## Dataset Preprocessing
Our project relies solely on the [lerobot library](https://github.com/huggingface/lerobot) (dataset `v2.0`), please follow their [installation instructions](https://github.com/huggingface/lerobot?tab=readme-ov-file#installation).
Here, we provide scripts for converting it to the lerobot format.
### Requirements
Requeir ffmpeg>=7.1(You can install with **conda install -y -c conda -forge ffmpeg**)
```
export SVT_LOG=0
python scripts/convert_to_lerobot.py --data_dir DATASET_FOLDER --save_dir SAVE_FOLDER --repo_id=agibot/agibotdigital --preprocess_video
## Example
# python scripts/convert_to_lerobot.py --data_dir final_format_data --save_dir ./output --repo_id=agibot/agibotdigital --preprocess_video
```
### Visualization
```
python scripts/visualize_dataset.py --repo-id='agibot/agibotdigital' --episode-index=0 --dataset-path=SAVE_FOLDER
## Example
# python scripts/visualize_dataset.py --repo-id='agibot/agibotdigital'--episode-index=0 --dataset-path=output
```
We sincerely thank the developers of lerobot for their exceptional contributions to the open-source community..
## Dataset Structure
### Folder hierarchy
```
data
├── observations
│ ├── digitaltwin_0 # This represents the task id.
│ │ ├── 9b21cf2e-829f-4aad-9b61-9edc5b947163 # This represents the episode uuid.
│ │ │ ├── depth # This is a folder containing depth information saved in PNG format.
│ │ │ ├── video # This is a folder containing videos from all camera perspectives.
│ │ ├── 131e407a-b828-4937-a554-e6706cbc5e2f
│ │ │ └── ...
│ │ └── ...
│ ├── digitaltwin_1
│ │ ├── 95808182-501f-4dca-984b-7404df844d31
│ │ │ ├── depth
│ │ │ ├── video
│ │ ├── edb0774b-13bb-4a8b-8bb0-71e82fe3ff6a
│ │ │ └── ...
│ └── ...
├── meta_info
│ ├── digitaltwin_0 # This represents the task id.
│ │ ├── 9b21cf2e-829f-4aad-9b61-9edc5b947163 # This represents the episode uuid.
│ │ │ ├── task_info.json # This represents the task information.
│ │ │ ├── proprio_meta_info.h5 # This file contains all the robot's proprioceptive information.
│ │ │ ├── camera_parameter.json # This contains all the cameras' intrinsic and extrinsic parameters.
│ │ ├── 131e407a-b828-4937-a554-e6706cbc5e2f
│ │ │ ├── task_info.json
│ │ │ ├── proprio_meta_info.h5
│ │ │ ├── camera_parameter.json
│ │ └── ...
│ └── digitaltwin_1
│ ├── 95808182-501f-4dca-984b-7404df844d31
│ │ │ ├── task_info.json
│ │ │ ├── proprio_meta_info.h5
│ │ │ ├── camera_parameter.json
│ └── edb0774b-13bb-4a8b-8bb0-71e82fe3ff6a
│ │ │ ├── task_info.json
│ │ │ ├── proprio_meta_info.h5
│ │ │ ├── camera_parameter.json
| └── ...
```
### json file format
In the `task_info.json` file, we store the basic information of every episode along with the language instructions. Here, we will further explain several specific keywords.
- **action_config**: The content corresponding to this key is a list composed of all **action slices** from the episode. Each action slice includes a start and end time, the corresponding atomic skill, and the language instruction.
- **key_frame**: The content corresponding to this key consists of annotations for keyframes, including the start and end times of the keyframes and detailed descriptions.
```
{
"episode_id": "9b21cf2e-829f-4aad-9b61-9edc5b947163",
"task_id": "digitaltwin_5",
"task_name": "pick_toys_into_box",
"init_scene_text": "",
"label_info": {
"objects": {
"extra_objects": [
{
"object_id": "omni6DPose_book_000",
"workspace_id": "book_table_extra"
}
],
"task_related_objects": [
{
"object_id": "omni6DPose_toy_motorcycle_023",
"workspace_id": "book_table_dual_left"
},
{
"object_id": "omni6DPose_toy_truck_030",
"workspace_id": "book_table_dual_right"
},
{
"object_id": "genie_storage_box_002",
"workspace_id": "book_table_dual_middle"
}
]
},
"action_config": [
{
"start_frame": 0,
"end_frame": 178,
"action_text": "",
"skill": "Pick",
"active_object": "gripper",
"passive_object": "omni6DPose_toy_motorcycle_023"
},
{
"start_frame": 179,
"end_frame": 284,
"action_text": "",
"skill": "Place",
"active_object": "omni6DPose_toy_motorcycle_023",
"passive_object": "genie_storage_box_002"
},
{
"start_frame": 285,
"end_frame": 430,
"action_text": "",
"skill": "Pick",
"active_object": "gripper",
"passive_object": "omni6DPose_toy_truck_030"
},
{
"start_frame": 431,
"end_frame": 536,
"action_text": "",
"skill": "Place",
"active_object": "omni6DPose_toy_truck_030",
"passive_object": "genie_storage_box_002"
}
],
"key_frame": []
}
}
```
### h5 file format
In the `proprio_stats.h5` file, we store all the robot's proprioceptive data. For more detailed information, please refer to the [explanation of proprioceptive state](#explanation-of-proprioceptive-state).
```
|-- timestamp
|-- state
|-- effector
|-- force
|-- index
|-- position
|-- end
|-- angular
|-- orientation
|-- position
|-- velocity
|-- wrench
|-- joint
|-- current_value
|-- effort
|-- position
|-- velocity
|-- robot
|-- orientation
|-- orientation_drift
|-- position
|-- position_drift
|-- action
|-- effector
|-- force
|-- index
|-- position
|-- end
|-- angular
|-- orientation
|-- position
|-- velocity
|-- wrench
|-- joint
|-- effort
|-- index
|-- position
|-- velocity
|-- robot
|-- index
|-- orientation
|-- position
|-- velocity
```
## Explanation of Proprioceptive State
### Terminology
*The definitions and data ranges in this section may change with software and hardware version. Stay tuned.*
**State and action**
1. State
State refers to the monitoring information of different sensors and actuators.
2. Action
Action refers to the instructions sent to the hardware abstraction layer, where controller would respond to these instructions. Therefore, there is a difference between the issued instructions and the actual executed state.
**Actuators**
1. ***Effector:*** refers to the end effector, for example dexterous hands or grippers.
2. ***End:*** refers to the 6DoF end pose on the robot flange.
4. ***Joint:*** refers to the joints of the robot, with 34 degrees of freedom (2 DoF head, 2 Dof waist, 7 DoF each arm, 8 Dof each gripper).
5. ***Robot:*** refers to the robot's pose in its surrouding environment. The orientation and position refer to the robot's relative pose in the odometry coordinate syste
### Common fields
1. Position: Spatial position, encoder position, angle, etc.
2. Velocity: Speed
3. Angular: Angular velocity
4. Effort: Torque of the motor. Not available for now.
5. Wrench: Six-dimensional force, force in the xyz directions, and torque. Not available for now.
### Value shapes and ranges
| Group | Shape | Meaning |
| --- | :---- | :---- |
| /timestamp | [N] | timestamp in seconds:nanoseconds in simulation time |
| /state/effector/position (gripper) | [N, 2] | left `[:, 0]`, right `[:, 1]`, gripper open range in mm |
| /state/end/orientation | [N, 2, 4] | left `[:, 0, :]`, right `[:, 1, :]`, flange quaternion with wxyz |
| /state/end/position | [N, 2, 3] | left `[:, 0, :]`, right `[:, 1, :]`, flange xyz in meters |
| /state/joint/position | [N, 34] | joint position based on joint names |
| /state/joint/velocity | [N, 34] | joint velocity based on joint names |
| /state/joint/effort | [N, 34] | joint effort based on joint names |
| /state/robot/orientation | [N, 4] | quaternion in wxyz |
| /state/robot/position | [N, 3] | xyz position, where z is always 0 in meters |
| /action/*/index | [M] | actions indexes refer to when the control source is actually sending signals |
| /action/effector/position (gripper) | [N, 2] | left `[:, 0]`, right `[:, 1]`, gripper open range in mm |
| /action/end/orientation | [N, 2, 4] | same as /state/end/orientation |
| /action/end/position | [N, 2, 3] | same as /state/end/position |
| /action/end/index | [M_2] | same as other indexes |
| /action/joint/position | [N, 14] | same as /state/joint/position |
| /action/joint/index | [M_4] | same as other indexes |
| /action/robot/velocity | [N, 2] | vel along x axis `[:, 0]`, yaw rate `[:, 1]` |
| /action/robot/index | [M_5] | same as other indexes |
# 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{contributors2025agibotdigitalworld,
title={AgiBot DigitalWorld},
author={Jiyao Zhang, Mingjie Pan, Baifeng Xie, Yinghao Zhao, Wenlong Gao, Guangte Xiang, Jiawei Zhang, Dong Li, Zhijun Li, Sheng Zhang, Hongwei Fan, Chengyue Zhao, Shukai Yang, Maoqing Yao, Chuanzhe Suo, Hao Dong},
howpublished={\url{https://agibot-digitalworld.com/}},
year={2025}
}
```