OS-ATLAS: A Foundation Action Model for Generalist GUI Agents

• URL: http://arxiv.org/abs/2410.23218v1
• Date: Wed, 30 Oct 2024 17:10:19 GMT
• Title: OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
• Authors: Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, Yu Qiao,
• Abstract summary: OS-Atlas is a foundational GUI action model that excels at GUI grounding and OOD agentic tasks. We are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements.
• Score: 55.37173845836839
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are often reluctant to use open-source VLMs due to their significant performance lag compared to their closed-source counterparts, particularly in GUI grounding and Out-Of-Distribution (OOD) scenarios. To facilitate future research in this area, we developed OS-Atlas - a foundational GUI action model that excels at GUI grounding and OOD agentic tasks through innovations in both data and modeling. We have invested significant engineering effort in developing an open-source toolkit for synthesizing GUI grounding data across multiple platforms, including Windows, Linux, MacOS, Android, and the web. Leveraging this toolkit, we are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements. This dataset, combined with innovations in model training, provides a solid foundation for OS-Atlas to understand GUI screenshots and generalize to unseen interfaces. Through extensive evaluation across six benchmarks spanning three different platforms (mobile, desktop, and web), OS-Atlas demonstrates significant performance improvements over previous state-of-the-art models. Our evaluation also uncovers valuable insights into continuously improving and scaling the agentic capabilities of open-source VLMs.

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