mbreuss/flower_calvin_abcd

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

This is a pretrained FlowerVLA model for robotic manipulation trained on the CALVIN ABCD dataset. Flower is an efficient Vision-Language-Action Flow policy for robot learning that only contains 1B parameters.

Model Description

FlowerVLA is a novel architecture that:

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

Model Performance

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

Train→Test	Method	1	2	3	4	5	Avg. Len.
{dataset_name}	FlowerVLA	99.1%	97.8%	95.2%	92.4%	87.8%	4.72

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

Usage

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)

Training Details

Configuration

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

@inproceedings{ reuss2025flower, # Add citation when available }

License

This model is released under the MIT license.