---
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 Spatial

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

avg_seq_len success rate 0.9681089520454407
pick_up_the_black_bowl_between_the_plate_and_the_ramekin_and_place_it_on_the_plate with success 0.9791666666666666
pick_up_the_black_bowl_next_to_the_ramekin_and_place_it_on_the_plate with success 0.9807692307692308
pick_up_the_black_bowl_from_table_center_and_place_it_on_the_plate with success 0.9807692307692308
pick_up_the_black_bowl_on_the_cookie_box_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_in_the_top_drawer_of_the_wooden_cabinet_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_on_the_ramekin_and_place_it_on_the_plate with success 0.8621794871794872
pick_up_the_black_bowl_next_to_the_cookie_box_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_on_the_stove_and_place_it_on_the_plate with success 1.0
pick_up_the_black_bowl_next_to_the_plate_and_place_it_on_the_plate with success 0.9166666666666666
pick_up_the_black_bowl_on_the_wooden_cabinet_and_place_it_on_the_plate with success 0.9615384615384616


### 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.