Dexbotic: Open-Source Vision-Language-Action Toolbox

Xie, Bin, Zhou, Erjin, Jia, Fan, Shi, Hao, Fan, Haoqiang, Zhang, Haowei, Li, Hebei, Sun, Jianjian, Bin, Jie, Huang, Junwen, Liu, Kai, Liu, Kaixin, Gu, Kefan, Sun, Lin, Zhang, Meng, Han, Peilong, Hao, Ruitao, Zhang, Ruitao, Huang, Saike, Xie, Songhan, Wang, Tiancai, Liu, Tianle, Tang, Wenbin, Zhu, Wenqi, Chen, Yang, Liu, Yingfei, Zhou, Yizhuang, Liu, Yu, Zhao, Yucheng, Ma, Yunchao, Wei, Yunfei, Chen, Yuxiang, Chen, Ze, Li, Zeming, Wu, Zhao, Zhang, Ziheng, Liu, Ziming, Yan, Ziwei, Zhang, Ziyu

arXiv.org Artificial Intelligence 

In this paper, we present Dexbotic, an open-source Vision-Language-Action (VLA) model toolbox based on Py-T orch. It aims to provide a one-stop VLA research service for professionals in the field of embodied intelligence. It offers a codebase that supports multiple mainstream VLA policies simultaneously, allowing users to reproduce various VLA methods with just a single environment setup. The toolbox is experiment-centric, where the users can quickly develop new VLA experiments by simply modifying the Exp script. Moreover, we provide much stronger pretrained models to achieve great performance improvements for state-of-the-art VLA policies. Dexbotic will continuously update to include more of the latest pre-trained foundation models and cutting-edge VLA models in the industry.

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