README.md

---
license: apache-2.0
task_categories:
  - robotics
  - visual-question-answering
tags:
  - gui-agent
  - mobile
  - grounding
  - android
  - static-evaluation
pretty_name: "AndroidControl*"
size_categories:
  - 1K<n<10K
---

# AndroidControl*

A curated step-level evaluation subset extracted from AndroidControl, used for static mobile GUI understanding evaluation in [UI-MOPD](https://huggingface.co/UI-MOPD) (Multi-platform On-Policy Distillation for Continual GUI Agent Learning).

## Overview

AndroidControl* contains 4,260 step-level records from 781 Android trajectories. Each record includes the trajectory identifier, step index, high-level task goal, per-step instruction, normalized action, screenshot path, screenshot resolution, and optional UI grounding metadata.

AndroidControl* preserves the same normalized mobile action space used in UI-MOPD training, including `click`, `scroll`, `input_text`, `open_app`, `wait`, `navigate_back`, `long_press`, and `navigate_home`. For actions that can be matched to a UI element, grounding metadata (target bounding boxes, widget class, text, resource id, package name, ancestor information, and matching node count) is included. This subset is mainly used to evaluate static mobile GUI understanding, verify action-screen alignment, and illustrate the format of mobile data after normalization.

## Dataset Statistics

| Metric | Value |
|--------|-------|
| Step Records | 4,260 |
| Trajectories | 781 |
| Platform | Android Mobile (1080x2400) |
| Format | JSONL + PNG screenshots |
| Coordinate System | Absolute pixel (actions normalized to [0, 1000] for evaluation) |

## Action Space

| Action | Description |
|--------|-------------|
| `click` | Tap at coordinate |
| `long_press` | Long press at coordinate |
| `scroll` | Scroll in direction (up/down/left/right) |
| `input_text` | Type text into active field |
| `open_app` | Launch application by name |
| `navigate_back` | Return to previous interface |
| `navigate_home` | Return to home screen |
| `wait` | Wait for UI response |

## Grounding Metadata

For actions that can be matched to a UI element, each record includes grounding metadata:

| Field | Description |
|-------|-------------|
| `target_bbox` | Bounding box of the target element [left, top, right, bottom] in pixels |
| `target_class` | Android widget class (e.g., `android.widget.TextView`) |
| `target_text` | Visible text on the element |
| `target_content_desc` | Accessibility content description |
| `target_resource_id` | Android resource ID |
| `target_package` | Application package name |
| `ancestor_bbox` | Bounding box of the nearest ancestor with text |
| `ancestor_text` | Text on the ancestor element |
| `n_matching_nodes` | Number of UI nodes matching the action target |

Steps without a directly groundable target (e.g., waiting or app-level operations) have `grounding: null`.

## Data Format

```
AndroidControl-Star/
  steps.jsonl              # All 4,260 step-level records
  images/
    episode_0/
      step_0.png
      step_1.png
      ...
    episode_100/
      ...
```

### JSONL Record Structure

```json
{
  "episode_id": 0,
  "step_idx": 0,
  "total_steps": 3,
  "goal": "Open the Zoho Meet app, view the scheduled meetings.",
  "instruction": "Open the Zoho Meet app",
  "action": {
    "action_type": "open_app",
    "app_name": "Zoho Meeting"
  },
  "screenshot": "images/episode_0/step_0.png",
  "screenshot_width": 1080,
  "screenshot_height": 2400,
  "grounding": {
    "target_bbox": [494, 365, 586, 416],
    "target_class": "android.widget.TextView",
    "target_text": "",
    "target_content_desc": "",
    "target_resource_id": "",
    "target_package": "com.zoho.meeting",
    "ancestor_bbox": [360, 317, 720, 464],
    "ancestor_text": "Past",
    "n_matching_nodes": 14
  }
}
```

## Evaluation Protocol

Coordinates are normalized to [0, 1000] for both model input and evaluation:

```python
# Normalize pixel coordinate to [0, 1000]
norm_x = round(x / width * 1000)
norm_y = round(y / height * 1000)

# Denormalize model output back to pixels
pixel_x = norm_x / 1000 * width
pixel_y = norm_y / 1000 * height
```

### Evaluation Metrics

| Metric | Description |
|--------|-------------|
| Action Type Accuracy | Predicted action type matches ground truth |
| Grounding (target) | Predicted coordinate falls within `target_bbox` |
| Grounding (ancestor) | Predicted coordinate falls within `ancestor_bbox` (more lenient) |
| Overall Accuracy | Joint accuracy of action type and grounding |

## Prompt Template

The evaluation uses the `mobile_use` tool interface with the following action set:

```
click, long_press, swipe, type, system_button, open_app, wait, terminate
```

Each step is prompted with the episode-level goal and the current step instruction. The model outputs structured reasoning (Thought + Action) followed by a tool call.

## Usage

```python
import json

with open("steps.jsonl", "r") as f:
    steps = [json.loads(line) for line in f]

# Group by episode
from collections import defaultdict
episodes = defaultdict(list)
for step in steps:
    episodes[step["episode_id"]].append(step)

print(f"Total steps: {len(steps)}, Episodes: {len(episodes)}")
```

Or clone directly:

```bash
git clone https://huggingface.co/datasets/UI-MOPD/AndroidControl-Star
```

## Citation

```bibtex
@article{ui-mopd,
  title={UI-MOPD: Multi-platform On-Policy Distillation for Continual GUI Agent Learning},
  year={2025}
}
```

## License

Apache 2.0