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Course Structure and Learning Path

This chapter outlines the complete 13-week learning journey through Physical AI and humanoid robotics. Understanding the course structure will help you plan your studies and track your progress effectively.

Learning Objectives​

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

  • Understand the four main modules and their relationships
  • Plan your learning schedule across 13 weeks
  • Identify prerequisites for each module
  • Recognize the progressive skill-building approach

Course Philosophy​

This course follows a learn-by-building approach:

  1. Start with fundamentals: Build strong foundations before advanced topics
  2. Hands-on practice: Every concept includes practical implementation
  3. Progressive complexity: Each module builds on previous knowledge
  4. Real-world focus: Skills directly applicable to robotics projects
  5. Open-source tools: Use industry-standard, accessible technologies

Four-Module Structure​

The course is organized into four comprehensive modules plus an introduction:

Introduction (Weeks 1-2)​

What you're reading now!

  • Understanding Physical AI
  • Humanoid robotics landscape
  • Course structure and planning
  • Development environment setup

Module 1: The Robotic Nervous System - ROS 2 (Weeks 3-5)​

Foundation for distributed robot control

Topics covered:

  • ROS 2 architecture and design principles
  • Nodes, topics, services, and actions
  • Building ROS 2 packages with Python
  • Launch files and parameter management
  • URDF for describing humanoid robots

Skills you'll gain:

  • Create ROS 2 nodes for robot control
  • Design communication patterns for complex behaviors
  • Configure robot descriptions
  • Launch and manage multi-node systems

Prerequisites:

  • Basic Python programming
  • Linux command line familiarity

Module 2: The Digital Twin - Gazebo & Unity (Weeks 6-7)​

Safe development and testing in simulation

Topics covered:

  • Gazebo simulation environment setup
  • Robot description formats (URDF/SDF)
  • Physics and sensor simulation
  • Unity for photorealistic visualization
  • Integrating Gazebo with ROS 2

Skills you'll gain:

  • Create realistic robot simulations
  • Design custom environments
  • Validate robot behavior safely
  • Transfer between simulation platforms

Prerequisites:

  • Module 1 completion (ROS 2 basics)
  • Understanding of robot kinematics

Module 3: The AI-Robot Brain - NVIDIA Isaac™ (Weeks 8-10)​

AI-powered perception and intelligent control

Topics covered:

  • NVIDIA Isaac SDK and Isaac Sim
  • AI-powered perception pipelines
  • Reinforcement learning for robot control
  • Sim-to-real transfer techniques
  • Hardware-accelerated VSLAM

Skills you'll gain:

  • Implement deep learning for robot perception
  • Train control policies with RL
  • Deploy AI models on edge devices
  • Bridge the sim-to-real gap

Prerequisites:

  • Modules 1-2 completion
  • Basic machine learning concepts
  • Python with numpy/pytorch

Module 4: Vision-Language-Action (VLA) (Weeks 11-13)​

Multimodal AI for natural human-robot interaction

Topics covered:

  • Voice-to-action with OpenAI Whisper
  • Large language models for cognitive planning
  • Multi-modal interaction design
  • Capstone project: Autonomous humanoid
  • Debugging and deployment

Skills you'll gain:

  • Integrate speech recognition
  • Use LLMs for task planning
  • Combine vision, language, and action
  • Deploy complete robot systems

Prerequisites:

  • Modules 1-3 completion
  • Understanding of transformer models
  • API integration experience

Weekly Breakdown​

Weeks 1-2: Introduction & Setup​

  • Understand Physical AI landscape
  • Set up development environment
  • Install ROS 2, Gazebo, required tools
  • Verify hardware/cloud setup

Weeks 3-5: ROS 2 Mastery​

  • Week 3: ROS 2 basics and communication
  • Week 4: Package development and URDF
  • Week 5: Advanced patterns and integration

Weeks 6-7: Simulation Expertise​

  • Week 6: Gazebo setup and robot simulation
  • Week 7: Unity visualization and Gazebo-ROS 2 integration

Weeks 8-10: AI Integration​

  • Week 8: Isaac SDK and perception
  • Week 9: Reinforcement learning
  • Week 10: Sim-to-real transfer

Weeks 11-13: Complete System​

  • Week 11: Voice and language integration
  • Week 12: Capstone project implementation
  • Week 13: Debugging, deployment, and showcase

Learning Resources​

Each Chapter Includes:​

  • Learning objectives: Clear goals for the session
  • Conceptual explanations: Understanding the "why"
  • Code examples: Practical implementations
  • Hands-on exercises: Practice problems
  • Assessment questions: Check your understanding

Additional Resources:​

  • Official documentation: ROS 2, NVIDIA Isaac, Gazebo
  • Code repository: All examples on GitHub
  • Community forum: Ask questions and share projects
  • Video tutorials: Supplementary visual guides

Hardware Options​

You can complete this course with different setups:

Workstation Requirements:

  • GPU: NVIDIA RTX 4070 Ti (12GB) minimum
  • CPU: Intel i7 13th Gen+ or AMD Ryzen 9
  • RAM: 64GB DDR5 (32GB minimum)
  • OS: Ubuntu 22.04 LTS

Robot Platform (for Module 4 capstone):

  • Unitree Go2 Edu ($1,800-$3,000)
  • Unitree G1 ($16,000)
  • Hiwonder TonyPi Pro ($600)
  • Or simulation-only completion

Option 2: Cloud Development​

Cloud Instance:

  • AWS g5.2xlarge or g6e.xlarge
  • ~$100-200 per month
  • Suitable for students without local GPU

Robot Platform:

  • Same options as above
  • Sim-to-real transfer via remote connection

Option 3: Simulation-Only​

  • Complete all modules in simulation
  • No physical robot required
  • Ideal for learning concepts
  • Can add robot later for deployment

Assessment & Milestones​

Module Assessments​

  • Multiple-choice questions after each chapter
  • Hands-on coding exercises
  • Mini-projects at module completion

Capstone Project (Module 4)​

Design and implement an autonomous humanoid robot system that:

  • Perceives its environment
  • Understands voice commands
  • Plans and executes tasks
  • Interacts naturally with humans

Time Commitment​

Expected effort per week:

  • Reading/Watching: 3-4 hours
  • Hands-on practice: 4-6 hours
  • Exercises/Projects: 3-5 hours
  • Total: 10-15 hours per week

Flexibility:

  • Self-paced learning supported
  • Can extend timeline as needed
  • Fast-track possible with prior experience

Success Strategies​

To get the most from this course:

  1. Practice consistently: Code along with examples
  2. Build incrementally: Complete each module before advancing
  3. Ask questions: Use community resources
  4. Document your work: Keep a learning journal
  5. Experiment: Try variations and extensions
  6. Collaborate: Share with peers
  7. Review regularly: Revisit earlier material

What Comes Next​

After completing this course, you'll be equipped to:

  • Build robot systems: End-to-end development capability
  • Contribute to open-source: Participate in ROS/robotics projects
  • Pursue advanced topics: Specialize in perception, control, or AI
  • Enter the field: Robotics engineering or research positions
  • Start your own projects: Create custom robotic applications

Summary​

This 13-week course takes you from Physical AI fundamentals through complete humanoid robot development. Four progressive modules cover ROS 2, simulation, AI integration, and multimodal interaction. With consistent effort and hands-on practice, you'll gain practical skills applicable to real-world robotics projects.

Next Steps​

You're now ready to begin Module 1! Head to "The Robotic Nervous System (ROS 2)" to start building the foundation of robot software architecture.

Estimated Reading Time: 12 minutes