AgiBotWorld-Alpha-CtrlWorld-327 Dataset

⚠️ Important: This dataset is processed from task_327 in Agibot-World-Alpha. For more details, please refer to the Acknowledgements section.

📑 Table of Contents

• 🚀 Get Started
  • Download the Dataset
• 📁 Data Structure
• 📊 Explanation of Proprioceptive State
  • State and Action
  • Common Fields
  • Value Shapes and Ranges
• 🤗 Acknowledgements
• 📄 License
• 💬 Contact

🚀 Get Started

Download the Dataset

To download the full dataset, you can use the following commands. 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/pyromind/AgiBotWorld-Alpha-CtrlWorld-327

# If you want to clone without large files - just their pointers
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/pyromind/AgiBotWorld-Alpha-CtrlWorld-327

The data has already been pre-processed and can be used directly with our inference code.

📁 Data Structure

The dataset is organized as follows:

task_327/
├── annotation/
│   ├── train/          # Training episode annotations (207 episodes)
│   │   ├── 0.json
│   │   ├── 1.json
│   │   └── ...
│   └── val/            # Validation episode annotations (2 episodes)
│       ├── 99.json
│       └── 199.json
├── latent_videos/
│   ├── train/          # Pre-encoded latent video representations
│   │   ├── 0/
│   │   │   ├── 0.pt
│   │   │   ├── 1.pt
│   │   │   └── 2.pt
│   │   └── ...
│   └── val/
│       └── ...
├── videos/
│   ├── train/          # Original video files
│   └── val/
└── stat.json           # Dataset statistics

📊 Explanation of Proprioceptive State

State and Action

State

The state represents the proprioceptive observations of the robot at each timestep. It includes:

• End-effector orientation: 4D quaternion representation (w, x, y, z) describing the orientation of the robot's end-effector
• End-effector position: 3D Cartesian coordinates (x, y, z) of the end-effector position
• Effector position: 3D Cartesian coordinates (x, y, z) of additional effector position information

The state vector has a dimension of 16, combining all these proprioceptive measurements.

Action

The action represents the control commands sent to the robot. In this dataset:

• Actions are 2-dimensional vectors
• Actions control the effector position in the environment

Common Fields

Each annotation JSON file ({episode_id}.json) contains the following fields:

Field	Type	Description
texts	List[str]	Task description and initial scene description
episode_id	int	Unique identifier for the episode
success	int	Binary indicator (0 or 1) whether the episode was successful
video_length	int	Number of frames in the processed video
raw_length	int	Number of frames in the original raw video
state_columns	List[str]	Column names for state components: ['observation.states.end.orientation', 'observation.states.end.position', 'observation.states.effector.position']
action_columns	List[str]	Column names for action components: ['actions.effector.position']
states	List[List[float]]	Array of state vectors, one per timestep. Each state is a 16-dimensional vector
actions	List[List[float]]	Array of action vectors, one per timestep. Each action is a 2-dimensional vector
videos	List[Dict]	List of video file paths, e.g., [{'video_path': 'videos/train/{episode_id}/{segment_id}.mp4'}]
latent_videos	List[str]	List of paths to pre-encoded latent video representations

Value Shapes and Ranges

State Values

• Shape: [T, 16] where T is the number of timesteps (video_length)
• Components:
  • Orientation (quaternion): 4 values, typically in range [-1.0, 1.0]
  • Positions: 6 values (3D end position + 3D effector position), typically in range [-1.0, 1.0] for normalized coordinates, or larger ranges for absolute positions (e.g., up to ~97.0)
• Overall range: Approximately [-0.86, 97.29] (values may vary depending on normalization)

Action Values

• Shape: [T, 2] where T is the number of timesteps
• Range: [0.0, 1.0] (normalized control values)

Latent Videos

• Format: PyTorch tensor files (.pt)
• Shape: [T, 4, 24, 40] where:
  • T: Number of frames (matches video_length)
  • 4: Number of channels
  • 24: Height dimension
  • 40: Width dimension

Dataset Statistics

• Training episodes: 207
• Validation episodes: 2
• Total episodes: 209

The stat.json file contains sample state and action values at the 1st and 99th percentiles, which can be used for normalization or data analysis purposes.

🤗 Acknowledgements

We would like to express our gratitude to the following projects and teams:

• AgiBot-World: This dataset is processed from task_327 in Agibot-World-Alpha. We acknowledge the OpenDriveLab team for their excellent work on the large-scale manipulation platform for scalable and intelligent embodied systems.

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📄 License

All the data and code within this repository are licensed under CC BY-NC-SA 4.0.

💬 Contact

For questions or suggestions, please contact us through the project Issues.