README.md

---
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.