File size: 1,746 Bytes
1ed3f7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ---
license: apache-2.0
library_name: vla-foundry
tags:
- foundry
- vla_foundry
- vla
- robotics
- diffusion-policy
- flow-matching
pipeline_tag: robotics
---
# Foundry-VLA-1.7B-sim
A 1.7B parameter vision-language-action model for bimanual robotic manipulation, part of the [VLA Foundry](https://github.com/TRI-ML/vla_foundry) collection. Trained on simulated manipulation data only.
## Model Description
- **Architecture:** Foundry-VLM-1.3B vision-language backbone + (condition on last 4 layers) flow-matching diffusion action head (24 layers, 1024 hidden dim, 16 heads)
- **Parameters:** 1.7B (non-embedding)
- **Action space:** 20-dim relative actions (bimanual xyz + 6D rotation + gripper)
- **Cameras:** 4 views (2 scene + 2 wrist)
- **Training data:** 102M samples from simulated bimanual manipulation tasks only
- **VLM backbone:** [Foundry-VLM-1.3B-200M](https://huggingface.co/TRI-ML/Foundry-VLM-1.3B-200M)
## Evaluation Results
Success rates on 16 seen tasks and 3 unseen tasks (200 rollouts per task):
| Simulator | Seen (16 tasks) | Unseen (3 tasks) |
|---|---|---|
| CS | 60.3% | 8.2% |
| OSS | 41.0% | 11.7% |
## Usage
```bash
git clone https://github.com/TRI-ML/vla_foundry.git
cd vla_foundry
pip install -e .
```
```python
from vla_foundry.models.base_model import BaseModel
model = BaseModel.from_pretrained("TRI-ML/Foundry-VLA-1.7B-sim")
```
## Links
- **Project page:** [tri-ml.github.io/vla_foundry](https://tri-ml.github.io/vla_foundry/)
- **Paper:** [VLA Foundry (arXiv 2604.19728)](https://arxiv.org/abs/2604.19728)
- **Code:** [github.com/TRI-ML/vla_foundry](https://github.com/TRI-ML/vla_foundry)
- **Collection:** [VLA Foundry collection](https://huggingface.co/collections/TRI-ML/vla-foundry)
|