Navigating the Digital World as Humans Do: Universal Visual Grounding
  for GUI Agents
Authors: name: Boyu Gou, name: Ruohan Wang, name: Boyuan Zheng, name: Yanan Xie, name: Cheng Chang, name: Yiheng Shu, name: Huan Sun, name: Yu Su

Multimodal large language models (MLLMs) are transforming the capabilities of
graphical user interface (GUI) agents, facilitating their transition from
controlled simulations to complex, real-world applications across various
platforms. However, the effectiveness of these agents hinges on the robustness
of their grounding capability. Current GUI agents predominantly utilize
text-based representations such as HTML or accessibility trees, which, despite
their utility, often introduce noise, incompleteness, and increased
computational overhead. In this paper, we advocate a human-like embodiment for
GUI agents that perceive the environment entirely visually and directly take
pixel-level operations on the GUI. The key is visual grounding models that can
accurately map diverse referring expressions of GUI elements to their
coordinates on the GUI across different platforms. We show that a simple
recipe, which includes web-based synthetic data and slight adaptation of the
LLaVA architecture, is surprisingly effective for training such visual
grounding models. We collect the largest dataset for GUI visual grounding so
far, containing 10M GUI elements and their referring expressions over 1.3M
screenshots, and use it to train UGround, a strong universal visual grounding
model for GUI agents. Empirical results on six benchmarks spanning three
categories (grounding, offline agent, and online agent) show that 1) UGround
substantially outperforms existing visual grounding models for GUI agents, by
up to 20% absolute, and 2) agents with UGround outperform state-of-the-art
agents, despite the fact that existing agents use additional text-based input
while ours only uses visual perception. These results provide strong support
for the feasibility and promises of GUI agents that navigate the digital world
as humans do.