GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone
  GUI Navigation
Authors: name: An Yan, name: Zhengyuan Yang, name: Wanrong Zhu, name: Kevin Lin, name: Linjie Li, name: Jianfeng Wang, name: Jianwei Yang, name: Yiwu Zhong, name: Julian McAuley, name: Jianfeng Gao, name: Zicheng Liu, name: Lijuan Wang

We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical
user interface (GUI) navigation task. MM-Navigator can interact with a
smartphone screen as human users, and determine subsequent actions to fulfill
given instructions. Our findings demonstrate that large multimodal models
(LMMs), specifically GPT-4V, excel in zero-shot GUI navigation through its
advanced screen interpretation, action reasoning, and precise action
localization capabilities. We first benchmark MM-Navigator on our collected iOS
screen dataset. According to human assessments, the system exhibited a 91\%
accuracy rate in generating reasonable action descriptions and a 75\% accuracy
rate in executing the correct actions for single-step instructions on iOS.
Additionally, we evaluate the model on a subset of an Android screen navigation
dataset, where the model outperforms previous GUI navigators in a zero-shot
fashion. Our benchmark and detailed analyses aim to lay a robust groundwork for
future research into the GUI navigation task. The project page is at
https://github.com/zzxslp/MM-Navigator.