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 SIMPLER results reported in the paper. Check out the pretraining codebase for testing.
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, 7/8)tensor representing delta EEF actions/Joint State 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)
@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.