docs/intro.md

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Introduction to Physical AI & Humanoid Robotics

Welcome to the comprehensive course on Physical AI & Humanoid Robotics. This course bridges the gap between digital AI systems and physical robot bodies, focusing on embodied intelligence and real-world interaction.

Course Overview

This course is structured into four core modules, each building upon the previous to provide a comprehensive understanding of humanoid robotics:

1. ROS 2 – The Robotic Nervous System: Learn the Robot Operating System 2, the middleware that connects all components of a robot.
2. Gazebo & Unity – The Digital Twin: Explore physics simulation and visualization tools for robot development and testing.
3. NVIDIA Isaac – The AI-Robot Brain: Understand AI frameworks that power autonomous robot behavior.
4. Vision-Language-Action (VLA) – The Complete System: Integrate perception, cognition, and action for complete robot autonomy.

What You'll Learn

By the end of this course, you will be able to:

• Design and implement humanoid robot control systems using ROS 2
• Create realistic simulations using Gazebo and Unity
• Deploy AI systems for perception and navigation using NVIDIA Isaac
• Integrate vision, language, and action systems for complete autonomy
• Implement an end-to-end autonomous humanoid system

Prerequisites

• Basic understanding of programming concepts (Python preferred)
• Familiarity with Linux command line
• Understanding of fundamental mathematics (linear algebra, calculus)
• Interest in robotics and AI systems

Getting Help

If you encounter any issues or have questions while going through the course material, you can:

1. Use the embedded RAG chatbot that can answer questions based on this course content
2. Refer to the official ROS 2, Gazebo, Unity, and Isaac documentation
3. Engage with the community through the links in the footer
4. Open an issue in the course repository on GitHub

Next Steps

Begin with Module 1: ROS 2 - The Robotic Nervous System to learn the foundational middleware that connects all robot components.