---
license: mit
base_model: Qwen/Qwen3-VL-4B-Instruct
library_name: transformers
datasets:
- TeleEmbodied/SMART-Data
tags:
- robotics
- vision-language-action
- embodied-ai
- manipulation
- simulation
- arxiv:2610.07652
---
# SMART-VLA
SMART-VLA is a PRTS-architecture vision-language-action model pretrained on [SMART-Data](https://huggingface.co/datasets/TeleEmbodied/SMART-Data) for articulated-object manipulation and sim-to-real transfer.
## Model Details
- **Base model:** [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct)
- **Architecture:** `PRTS_Qwen3VL`
- **Training data:** [SMART-Data](https://huggingface.co/datasets/TeleEmbodied/SMART-Data)
- **Parameters:** approximately 4.44B
- **Precision:** `bfloat16`
- **Action chunk size:** 50
- **Maximum action dimension:** 32
- **Released checkpoint:** `checkpoint-final-278972`
## Links
- **Paper:** [SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining](https://arxiv.org/abs/2610.07652)
- **Project page:** [https://teamillusion-smart.github.io/](https://teamillusion-smart.github.io/)
- **Dataset:** [TeleEmbodied/SMART-Data](https://huggingface.co/datasets/TeleEmbodied/SMART-Data)
- **Code and loading instructions:** [TeleHuman/PRTS](https://github.com/TeleHuman/PRTS)
## Usage
Please follow the installation, loading, and inference instructions in the [PRTS repository](https://github.com/TeleHuman/PRTS).
## Intended Use
SMART-VLA is intended for research on vision-language-action pretraining, articulated-object manipulation, and simulation-to-real robot learning.
## License
This model is released under the MIT License.
## Citation
```bibtex
@article{SMART2026ao,
title={SMART: Zero-Shot Sim-to-Real Articulated Object Manipulation via Large-Scale Synthetic Pretraining},
author={Jicong Ao and Shuhan Jiang and Yuling Zhong and Yanwen Liu and Yuhan Gao and Jiangyuan Zhao and Yang Zhang and Shiqiang Zhu and Chenjia Bai and Xuelong Li},
year={2026},
eprint={2610.07652},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2610.07652},
}
```