VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action
  Model
Authors: name: Yihao Wang, name: Pengxiang Ding, name: Lingxiao Li, name: Can Cui, name: Zirui Ge, name: Xinyang Tong, name: Wenxuan Song, name: Han Zhao, name: Wei Zhao, name: Pengxu Hou, name: Siteng Huang, name: Yifan Tang, name: Wenhui Wang, name: Ru Zhang, name: Jianyi Liu, name: Donglin Wang

Vision-Language-Action (VLA) models typically bridge the gap between
perceptual and action spaces by pre-training a large-scale Vision-Language
Model (VLM) on robotic data. While this approach greatly enhances performance,
it also incurs significant training costs. In this paper, we investigate how to
effectively bridge vision-language (VL) representations to action (A). We
introduce VLA-Adapter, a novel paradigm designed to reduce the reliance of VLA
models on large-scale VLMs and extensive pre-training. To this end, we first
systematically analyze the effectiveness of various VL conditions and present
key findings on which conditions are essential for bridging perception and
action spaces. Based on these insights, we propose a lightweight Policy module
with Bridge Attention, which autonomously injects the optimal condition into
the action space. In this way, our method achieves high performance using only
a 0.5B-parameter backbone, without any robotic data pre-training. Extensive
experiments on both simulated and real-world robotic benchmarks demonstrate
that VLA-Adapter not only achieves state-of-the-art level performance, but also
offers the fast inference speed reported to date. Furthermore, thanks to the
proposed advanced bridging paradigm, VLA-Adapter enables the training of a
powerful VLA model in just 8 hours on a single consumer-grade GPU, greatly
lowering the barrier to deploying the VLA model. Project page:
https://vla-adapter.github.io/.