---
license: other
license_name: dinov3-license
license_link: https://huggingface.co/PruhaNLP/TurboVLA-base/blob/main/DINOv3_LICENSE.md
base_model: H-EmbodVis/TurboVLA
pipeline_tag: robotics
library_name: pytorch
tags:
- robotics
- vision-language-action
- turbovla
- so100
- lerobot
- dinov3
- bert
arxiv: "2607.27205"
---

# TurboVLA-base (SO-100)

A ready-to-fine-tune [TurboVLA](https://arxiv.org/abs/2607.27205) checkpoint for the SO-100 / SO-101 arm, in the spirit of `lerobot/smolvla_base`.
No manual model assembly: one folder holds the config, the full weights (DINOv3 ViT-B + BERT + interaction + action decoder) and the tokenizer.

TurboVLA drops the LLM from the VLA loop. DINOv3 encodes each camera, BERT encodes the instruction, 6 bidirectional
vision-language cross-attention layers fuse them, and an ACT-style decoder predicts a 12-step continuous action chunk in one pass
(~0.2B params, L1 behavior cloning).

## How it was made

Built by [`model/build_turbovla_base.py`](https://github.com/PruhaNLP/robosim-at-home/blob/main/model/build_turbovla_base.py) from the official LIBERO checkpoint
[`H-EmbodVis/TurboVLA/checkpoints/libero/turbovla_libero.pth`](https://huggingface.co/H-EmbodVis/TurboVLA), the same starting point the paper uses for real-robot fine-tuning.
Every tensor is copied as is, except the embodiment-specific ones, which are freshly initialized (seed 0):

- `action_head.state_projection.net.0.weight`
- `action_head.state_projection.net.0.bias`
- `action_head.state_projection.net.1.weight`
- `action_head.state_projection.net.1.bias`
- `action_head.decoder.action_projection.layers.2.weight`
- `action_head.decoder.action_projection.layers.2.bias`

## I/O contract

| | |
| --- | --- |
| Cameras | 2 RGB views, order `front, wrist`, resized + padded to 256x256, ImageNet mean/std |
| Instruction | English text, BERT uncased, padded to 32 tokens |
| State | 6-D SO-100 joint positions, normalized with mean/std |
| Action | 12 x 6 absolute joint targets, `tanh` output mapped from per-joint dataset min/max |

This base has **no normalization stats**: they come from your dataset on the first fine-tune and are saved as `stats.safetensors`.
The paper recipe freezes BERT and trains everything else with lr 5e-5, AdamW (0.9, 0.95), L1 loss.

## Use it

In [robosim-at-home](https://github.com/PruhaNLP/robosim-at-home): switch **Mode → TurboVLA**, open **Train**, pick `PruhaNLP/TurboVLA-base`, tick datasets, start.
Inference and Eval then list the fine-tuned runs.

Standalone, with [`model/turbovla.py`](https://github.com/PruhaNLP/robosim-at-home/blob/main/model/turbovla.py):

```python
from model.turbovla import TurboEngine

engine = TurboEngine(device="cuda")
engine.load("PruhaNLP/TurboVLA-base")          # config + weights + tokenizer from this repo
# after fine-tuning (stats present):
chunk = engine.predict_chunk({"front": front_pil, "wrist": wrist_pil}, joints, "pick up the cube and put it in the tray")
```

## Files

| File | Content |
| --- | --- |
| `config.json` | `type: turbovla`, cameras, sizes, interaction / action head, full DINOv3 and BERT configs |
| `model.safetensors` | fp32 weights; key names match the official `turbovla.models.turbovla.TurboVLA` module (transformers 4.x DINOv3 layout `vision_encoder.backbone.layer.N`) |
| `tokenizer.json`, `tokenizer_config.json` | `google-bert/bert-base-uncased` tokenizer |
| `DINOv3_LICENSE.md`, `LICENSE` | licenses inherited from `H-EmbodVis/TurboVLA` |

The weights load strictly into the official TurboVLA class built with `action_dim=6`, `state_dim=6`, `num_views=2`,
`image_size=256`, `text.padding_length=32`, and give the same outputs as `model/turbovla.py`.

## License

Weights contain DINOv3-derived parameters and are distributed under the [DINOv3 License](DINOv3_LICENSE.md); TurboVLA code is Apache-2.0.

## Citation

```bibtex
@article{xie2026turbovla,
  title   = {TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM},
  author  = {Xie, Hengyi and Yao, Chenfei and Wu, Xianjin and Xi, Xuanyang and Tang, Yiping and Xu, Di and
             Zhu, Yingying and Liang, Dingkang and Bai, Xiang and Ding, Han},
  journal = {arXiv preprint arXiv:2607.27205},
  year    = {2026}
}
```