Training Computer-Use Models: Creating Human Trajectories with C/ua

Published on May 1, 2025 by The Cua Team

In our previous posts, we covered building your own Computer-Use Operator and using the Agent framework to simplify development. Today, we'll focus on a critical aspect of improving computer-use agents and models: gathering high-quality demonstration data using C/ua's Computer-Use Interface (CUI) and its Gradio UI to create and share human-generated trajectories.

Why is this important? Underlying models used by Computer-use agents need examples of how humans interact with computers to learn effectively. By creating a dataset of diverse, well-executed tasks, we can help train better models that understand how to navigate user interfaces and accomplish real tasks.

What You'll Learn

By the end of this tutorial, you'll be able to:

Set up the Computer-Use Interface (CUI) with Gradio UI support
Record your own computer interaction trajectories
Organize and tag your demonstrations
Upload your datasets to Hugging Face for community sharing
Contribute to improving computer-use AI for everyone

Prerequisites:

macOS Sonoma (14.0) or later
Python 3.10+
Basic familiarity with Python and terminal commands
A Hugging Face account (for uploading datasets)

Estimated Time: 20-30 minutes

Understanding Human Trajectories

What are Human Trajectories?

Human trajectories, in the context of Computer-use AI Agents, are recordings of how humans interact with computer interfaces to complete tasks. These interactions include:

Mouse movements, clicks, and scrolls
Keyboard input
Changes in the UI state
Time spent on different elements

These trajectories serve as examples for AI models to learn from, helping them understand the relationship between:

The visual state of the screen
The user's goal or task
The most appropriate action to take

Why Human Demonstrations Matter

Unlike synthetic data or rule-based automation, human demonstrations capture the nuanced decision-making that happens during computer interaction:

Natural Pacing: Humans pause to think, accelerate through familiar patterns, and adjust to unexpected UI changes
Error Recovery: Humans demonstrate how to recover from mistakes or handle unexpected states
Context-Sensitive Actions: The same UI element might be used differently depending on the task context

By contributing high-quality demonstrations, you're helping to create more capable, human-like computer-use AI systems.

Setting Up Your Environment

Installing the CUI with Gradio Support

The Computer-Use Interface includes an optional Gradio UI specifically designed to make recording and sharing demonstrations easy. Let's set it up:

Create a Python environment (optional but recommended):

bash
# Using conda
conda create -n cua-trajectories python=3.10
conda activate cua-trajectories

# Using venv
python -m venv cua-trajectories
source cua-trajectories/bin/activate  # On macOS/Linux

Install the CUI package with UI support:

bash
pip install "cua-computer[ui]"

Set up your Hugging Face access token: Create a .env file in your project directory and add your Hugging Face token:
```
bash
echo "HF_TOKEN=your_huggingface_token" > .env
```
You can get your token from your Hugging Face account settings.

Understanding the Gradio UI

The Computer-Use Interface Gradio UI provides three main components:

Recording Panel: Captures your screen, mouse, and keyboard activity during demonstrations
Review Panel: Allows you to review, tag, and organize your demonstration recordings
Upload Panel: Lets you share your demonstrations with the community via Hugging Face

The UI is designed to make the entire process seamless, from recording to sharing, without requiring deep technical knowledge of the underlying systems.

Creating Your First Trajectory Dataset

Launching the UI

To get started, create a simple Python script to launch the Gradio UI:

python
# launch_trajectory_ui.py
from computer.ui.gradio.app import create_gradio_ui
from dotenv import load_dotenv

# Load your Hugging Face token from .env
load_dotenv('.env')

# Create and launch the UI
app = create_gradio_ui()
app.launch(share=False)

Run this script to start the UI:

bash
python launch_trajectory_ui.py

Recording a Demonstration

Let's walk through the process of recording your first demonstration:

Start the VM: Click the "Initialize Computer" button in the UI to initialize a fresh macOS sandbox. This ensures your demonstrations are clean and reproducible.
Perform a Task: Complete a simple task like creating a document, organizing files, or searching for information. Natural, everyday tasks make the best demonstrations.
Review Recording: Click the "Conversation Logs" or "Function Logs" tabs to review your captured interactions, making sure there is no personal information that you wouldn't want to share.
Add Metadata: In the "Save/Share Demonstrations" tab, give your recording a descriptive name (e.g., "Creating a Calendar Event") and add relevant tags (e.g., "productivity", "time-management").
Save Your Demonstration: Click "Save" to store your recording locally.

Key Tips for Quality Demonstrations

To create the most valuable demonstrations:

Start and end at logical points: Begin with a clear starting state and end when the task is visibly complete
Narrate your thought process: Use the message input to describe what you're trying to do and why
Move at a natural pace: Don't rush or perform actions artificially slowly
Include error recovery: If you make a mistake, keep going and show how to correct it
Demonstrate variations: Record multiple ways to complete the same task

Organizing and Tagging Demonstrations

Effective tagging and organization make your demonstrations more valuable to researchers and model developers. Consider these tagging strategies:

Task-Based Tags

Describe what the demonstration accomplishes:

web-browsing
document-editing
file-management
email
scheduling

Application Tags

Identify the applications used:

finder
safari
notes
terminal
calendar

Complexity Tags

Indicate the difficulty level:

beginner
intermediate
advanced
multi-application

UI Element Tags

Highlight specific UI interactions:

drag-and-drop
menu-navigation
form-filling
search

The Computer-Use Interface UI allows you to apply and manage these tags across all your saved demonstrations, making it easy to create cohesive, well-organized datasets.

Uploading to Hugging Face

Sharing your demonstrations helps advance research in computer-use AI. The Gradio UI makes uploading to Hugging Face simple:

Preparing for Upload

Review Your Demonstrations: Use the review panel to ensure all demonstrations are complete and correctly tagged.
Select Demonstrations to Upload: You can upload all demonstrations or filter by specific tags.
Configure Dataset Information:
- Repository Name: Format as {your_username}/{dataset_name}, e.g., johndoe/productivity-tasks
- Visibility: Choose public to contribute to the community or private for personal use
- License: Standard licenses like CC-BY or MIT are recommended for public datasets

The Upload Process

Click "Upload to Hugging Face": This initiates the upload preparation.
Review Dataset Summary: Confirm the number of demonstrations and total size.
Confirm Upload: The UI will show progress as files are transferred.
Receive Confirmation: Once complete, you'll see a link to your new dataset on Hugging Face.

Your uploaded dataset will have a standardized format with the following structure:

json
{
  "timestamp": "2025-05-01T09:20:40.594878",
  "session_id": "1fe9f0fe-9331-4078-aacd-ec7ffb483b86",
  "name": "penguin lemon forest",
  "tool_calls": [...],  // Detailed interaction records
  "messages": [...],    // User/assistant messages
  "tags": ["highquality", "tasks"],
  "images": [...]       // Screenshots of each state
}

This structured format makes it easy for researchers to analyze patterns across different demonstrations and build better computer-use models.

python
from computer import Computer

computer = Computer(os="macos", display="1024x768", memory="8GB", cpu="4")
try:
    await computer.run()
    
    screenshot = await computer.interface.screenshot()
    with open("screenshot.png", "wb") as f:
        f.write(screenshot)
    
    await computer.interface.move_cursor(100, 100)
    await computer.interface.left_click()
    await computer.interface.right_click(300, 300)
    await computer.interface.double_click(400, 400)

    await computer.interface.type("Hello, World!")
    await computer.interface.press_key("enter")

    await computer.interface.set_clipboard("Test clipboard")
    content = await computer.interface.copy_to_clipboard()
    print(f"Clipboard content: {content}")
finally:
    await computer.stop()

Example: Shopping List Demonstration

Let's walk through a concrete example of creating a valuable demonstration:

Task: Adding Shopping List Items to a Doordash Cart

Start Recording: Begin with a clean desktop and a text file containing a shopping list.
Task Execution: Open the file, read the list, open Safari, navigate to Doordash, and add each item to the cart.
Narration: Add messages like "Reading the shopping list" and "Searching for rice on Doordash" to provide context.
Completion: Verify all items are in the cart and end the recording.
Tagging: Add tags like shopping, web-browsing, task-completion, and multi-step.

This type of demonstration is particularly valuable because it showcases real-world task completion requiring multiple applications and context switching.

Exploring Community Datasets

You can also learn from existing trajectory datasets contributed by the community:

Visit Hugging Face Datasets tagged with 'cua'
Explore different approaches to similar tasks
Download and analyze high-quality demonstrations

Conclusion

Summary

In this guide, we've covered how to:

Set up the Computer-Use Interface with Gradio UI
Record high-quality human demonstrations
Organize and tag your trajectories
Share your datasets with the community

By contributing your own demonstrations, you're helping to build more capable, human-like AI systems that can understand and execute complex computer tasks.

Next Steps

Now that you know how to create and share trajectories, consider these advanced techniques:

Create themed collections around specific productivity workflows
Collaborate with others to build comprehensive datasets
Use your datasets to fine-tune your own computer-use models

Resources

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