ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2510.23511
72
0

Dexbotic: Open-Source Vision-Language-Action Toolbox

27 October 2025
Bin Xie
Erjin Zhou
Fan Jia
Hao Shi
Haoqiang Fan
H. Zhang
Hebei Li
Jianjian Sun
Jie Bin
J. Huang
Kai-Chun Liu
Kaixin Liu
Kefan Gu
Lin Sun
Meng Zhang
Peilong Han
Ruitao Hao
Ruitao Zhang
Saike Huang
Songhan Xie
T. Wang
Tianle Liu
Wenbin Tang
Wenqi Zhu
Y. Chen
Yingfei Liu
Yizhuang Zhou
Yu Liu
Yucheng Zhao
Yunchao Ma
Y. X. Wei
Y. Chen
Z. Chen
Zeming Li
Zhao Wu
Ziheng Zhang
Ziming Liu
Ziwei Yan
Z. Zhang
    LM&RoVLM
ArXiv (abs)PDFHTMLGithub (282★)
Main:8 Pages
9 Figures
Bibliography:2 Pages
5 Tables
Appendix:1 Pages
Abstract

In this paper, we present Dexbotic, an open-source Vision-Language-Action (VLA) model toolbox based on PyTorch. 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.

View on arXiv
Comments on this paper