VITRA-VLA-3B
VITRA is a novel approach for pretraining Vision-Language-Action (VLA) models for robotic manipulation using large-scale, unscripted, real-world videos of human hand activities. Treating human hand as dexterous robot end-effector, we show that in-the-wild egocentric human videos without any annotations can be transformed into data formats fully aligned with existing robotic V-L-A training data in terms of task granularity and labels. We create a human hand V-L-A dataset containing over 1 million episodes. We further develop a VLA model with a causal action transformer trained on this dataset. It demonstrates strong zero-shot human-hand action prediction in entirely new scenes and serves as a cornerstone for few-shot finetuning and adaptation to real-world robotic manipulation.
All our code, pre-trained model weights, are licensed under the MIT license.
Please refer to our project page and paper for more details.
Model Summary
- Developed by: The VITRA team consisting of researchers from Microsoft Research.
- Model type: Vision-Language-Action (language, image => robot actions)
- Language(s) (NLP): en
- License: MIT
- Pretraining Dataset: VITRA-1M
- Repository: https://github.com/microsoft/ViTra
- Paper: Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos
- Project Page: https://microsoft.github.io/VITRA/
Citation
@article{li2025vitra,
title={Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos},
author={Qixiu Li and Yu Deng and Yaobo Liang and Lin Luo and Lei Zhou and Chengtang Yao and Lingqi Zeng and Zhiyuan Feng and Huizhi Liang and Sicheng Xu and Yizhong Zhang and Xi Chen and Hao Chen and Lily Sun and Dong Chen and Jiaolong Yang and Baining Guo},
journal={arXiv preprint arXiv:2510.21571},
year={2025}
}