Flower Calvin Abcd

🚀 FlowerVLA - Vision-Language-Action Flow Model for CALVIN ABCD

A pre - trained FlowerVLA model for robotic manipulation, trained on the CALVIN ABCD dataset. Flower is an efficient Vision - Language - Action Flow policy for robot learning with only 1B parameters.

🚀 Quick Start

Check out our full model implementation on Github todo and follow the instructions in the readme to test the model on one of the environments.

obs = {
    "rgb_obs": {
        "rgb_static": static_image,
        "rgb_gripper": gripper_image
    }
}
goal = {"lang_text": "pick up the blue cube"}
action = model.step(obs, goal)

✨ Features

FlowerVLA is a novel architecture that:

• Uses half of Florence - 2 for multi - modal vision - language encoding
• Employs a novel transformer - based flow matching architecture
• Provides an efficient, versatile VLA policy with only ~1B parameters

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

obs = {
    "rgb_obs": {
        "rgb_static": static_image,
        "rgb_gripper": gripper_image
    }
}
goal = {"lang_text": "pick up the blue cube"}
action = model.step(obs, goal)

📚 Documentation

Model Performance

This checkpoint contains weights for the CALVIN ABCD challenge and currently ranks 1 with the following results:

Input/Output Specifications

Inputs

• RGB Static Camera: (B, T, 3, H, W) tensor
• RGB Gripper Camera: (B, T, 3, H, W) tensor
• Language Instructions: Text strings

Outputs

• Action Space: (B, T, 7) tensor representing delta EEF actions

Training Details

Configuration

• Optimizer: AdamW
• Learning Rate: 2e - 5
• Weight Decay: 0.05

🔧 Technical Details

No specific technical details beyond what is already covered are provided, so this section is skipped.

📄 License

This model is released under the MIT license.

@inproceedings{ reuss2025flower, # Add citation when available }