OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
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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Zhiyong Wu
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Zhiyong Wu
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Zhenyu Wu
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Fangzhi Xu
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Fangzhi Xu
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Yian Wang
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Qiushi Sun
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Qiushi
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QiushiSun
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Chengyou Jia
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ChengyouJia
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ChengyouJia
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Kanzhi Cheng
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Kanzhi Cheng
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Zichen Ding
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2024-11-04T09:35:44.330Z
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Zichen Ding
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heroding77
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Liheng Chen
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2024-11-04T14:19:29.363Z
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LIHENG CHEN
user
Lemaqwq
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Paul Pu Liang
67286aaf6c2c5bca2617d7be
Yu Qiao
OS-ATLAS: A Foundation Action Model for Generalist GUI Agents
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.
OS-Atlas improves GUI agent performance through a large open-source GUI grounding dataset and model innovations.
GUI agents Vision-Language Models (VLMs) GUI grounding Out-Of-Distribution (OOD) open-source toolkit GUI screenshots benchmarks