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---
language:
- en
tags:
- computer_use
- agents
- grounding
- multimodal
- ui-vision
- GroundCUA
size_categories:
- "1M<n<10M"
license: mit
task_categories:
- image-to-text
---
<!-- <p align="center">
<img src="assets/groundcua-hq.png" width="100%" alt="GroundCUA Overview">
</p> -->
<h1 align="center" style="font-size:42px; font-weight:700;">
GroundCUA: Grounding Computer Use Agents on Human Demonstrations
</h1>
<p align="center">
🌐 <a href="https://groundcua.github.io">Website</a> |
📑 <a href="https://arxiv.org/abs/2511.07332">Paper</a> |
🤗 <a href="https://huggingface.co/datasets/ServiceNow/GroundCUA">Dataset</a> |
🤖 <a href="https://huggingface.co/ServiceNow/GroundNext-7B-V0">Models</a>
</p>
<p align="center">
<img src="assets/groundcua-hq.png" width="100%" alt="GroundCUA Overview">
</p>
# GroundCUA Dataset
GroundCUA is a large and diverse dataset of real UI screenshots paired with structured annotations for building multimodal computer use agents. It covers **87 software platforms** across productivity tools, browsers, creative tools, communication apps, development environments, and system utilities. GroundCUA is designed for research on GUI grounding, UI perception, and vision-language-action models that interact with computers.
---
## Highlights
- **87 platforms** spanning Windows, macOS, Linux, and cross-platform apps
- **Annotated UI elements** with bounding boxes, text, and coarse semantic categories
- **SHA-256 file pairing** between screenshots and JSON annotations
- **Supports research on GUI grounding, multimodal agents, and UI understanding**
- **MIT license** for broad academic and open source use
---
## Dataset Structure
```
GroundCUA/
├── data/ # JSON annotation files
├── images/ # Screenshot images
└── README.md
```
### Directory Layout
Each platform appears as a directory name inside both `data/` and `images/`.
- `data/PlatformName/` contains annotation JSON files
- `images/PlatformName/` contains corresponding PNG screenshots
Image and annotation files share the same SHA-256 hash.
---
## File Naming Convention
Each screenshot has a matching annotation file using the same hash:
- `data/PlatformName/[hash].json`
- `images/PlatformName/[hash].png`
This structure ensures:
- Unique identifiers for each screenshot
- Easy pairing between images and annotations
- Compatibility with pipelines that expect hash-based addressing
---
## Annotation Format
Each annotation file is a list of UI element entries describing visible elements in the screenshot.
```json
[
{
"image_path": "PlatformName/screenshot_hash.png",
"bbox": [x1, y1, x2, y2],
"text": "UI element text",
"category": "Element category",
"id": "unique-id"
}
]
```
### Field Descriptions
**image_path**
Relative path to the screenshot.
**bbox**
Bounding box coordinates `[x1, y1, x2, y2]` in pixel space.
**text**
Visible text or a short description of the element.
**category**
Coarse UI type label. Present only for some elements.
**id**
Unique identifier for the annotation entry.
---
## UI Element Categories
Categories are approximate and not guaranteed for all elements. Examples include:
- **Button**
- **Menu**
- **Input Elements**
- **Navigation**
- **Sidebar**
- **Visual Elements**
- **Information Display**
- **Others**
These labels provide light structure for UI grounding tasks but do not form a full ontology.
---
## Example Use Cases
GroundCUA can be used for:
- Training computer use agents to perceive and understand UI layouts
- Building GUI grounding modules for VLA agents
- Pretraining screen parsing and UI element detectors
- Benchmarking OCR, layout analysis, and cross-platform UI parsing
- Developing models that map UI regions to natural language or actions
---
## Citation
If you use GroundCUA in your research, please cite our work:
```bibtex
@misc{feizi2025groundingcomputeruseagents,
title={Grounding Computer Use Agents on Human Demonstrations},
author={Aarash Feizi and Shravan Nayak and Xiangru Jian and Kevin Qinghong Lin and Kaixin Li and Rabiul Awal and Xing Han Lù and Johan Obando-Ceron and Juan A. Rodriguez and Nicolas Chapados and David Vazquez and Adriana Romero-Soriano and Reihaneh Rabbany and Perouz Taslakian and Christopher Pal and Spandana Gella and Sai Rajeswar},
year={2025},
eprint={2511.07332},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2511.07332},
}
```
## License
GroundCUA is released under the MIT License.
Users are responsible for ensuring compliance with all applicable laws and policies. |