flower_calvin_abc

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

This is a pretrained FlowerVLA model for robotic manipulation trained on the CALVIN ABC 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 ABC 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

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, title={{FLOWER}: Democratizing Generalist Robot Policies with Efficient Vision-Language-Flow Models}, author={Moritz Reuss and Hongyi Zhou and Marcel R{"u}hle and {"O}mer Erdin{\c{c}} Ya{\u{g}}murlu and Fabian Otto and Rudolf Lioutikov}, booktitle={9th Annual Conference on Robot Learning}, year={2025}, url={https://openreview.net/forum?id=JeppaebLRD} }

License

This model is released under the MIT license.