---
license: mit
language:
- en
base_model:
- microsoft/Florence-2-large
pipeline_tag: robotics
tags:
- VLA
- LIBERO
- Robotics
- Flow
---
# FlowerVLA - Vision-Language-Action Flow Model finetuned on LIBERO Goal

This is a pretrained FlowerVLA model for robotic manipulation trained on the LIBERO Goal 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 LIBERO Goal challenge and achieves these results:

avg_seq_len success rate 0.9665064215660095
open_the_middle_drawer_of_the_cabinet with success 0.9807692307692308
put_the_bowl_on_the_stove with success 1.0
put_the_wine_bottle_on_top_of_the_cabinet with success 0.9807692307692308
sr_open_the_top_drawer_and_put_the_bowl_inside with success 0.842948717948718
sr_put_the_bowl_on_top_of_the_cabinet with success 1.0
push_the_plate_to_the_front_of_the_stove with success 0.9407051282051282
put_the_cream_cheese_in_the_bowl with success 0.9807692307692308
turn_on_the_stove with success 1.0
put_the_bowl_on_the_plate with success 1.0
put_the_wine_bottle_on_the_rack with success 0.9391025641025641

### 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.

```python
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.