[2306.00245] From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
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Computer Science > Machine Learning

arXiv:2306.00245 (cs)
[Submitted on 31 May 2023 (v1), last revised 6 Dec 2023 (this version, v2)]

Title:From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces

Authors:Peter Shaw, Mandar Joshi, James Cohan, Jonathan Berant, Panupong Pasupat, Hexiang Hu, Urvashi Khandelwal, Kenton Lee, Kristina Toutanova
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Abstract:Much of the previous work towards digital agents for graphical user interfaces (GUIs) has relied on text-based representations (derived from HTML or other structured data sources), which are not always readily available. These input representations have been often coupled with custom, task-specific action spaces. This paper focuses on creating agents that interact with the digital world using the same conceptual interface that humans commonly use -- via pixel-based screenshots and a generic action space corresponding to keyboard and mouse actions. Building upon recent progress in pixel-based pretraining, we show, for the first time, that it is possible for such agents to outperform human crowdworkers on the MiniWob++ benchmark of GUI-based instruction following tasks.
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2306.00245 [cs.LG]
  (or arXiv:2306.00245v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2306.00245

Submission history

From: Peter Shaw [view email]
[v1] Wed, 31 May 2023 23:39:18 UTC (2,076 KB)
[v2] Wed, 6 Dec 2023 23:46:36 UTC (2,077 KB)
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