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Welcome to Physical AI & Humanoid Robotics

A Comprehensive Guide to Building Intelligent Physical Systems

🎯 What You'll Learn​

This book teaches you to build intelligent physical AI systems that can:

  • 🤖 Navigate autonomously in complex environments
  • 👁️ Perceive and understand their surroundings using vision
  • 🤝 Manipulate objects and interact safely with humans
  • 🚶 Walk on two legs (bipedal locomotion)
  • 💬 Understand and respond to natural language commands
  • 🧠 Make decisions using vision-language-action (VLA) models

📚 Course Structure​

16-Week Learning Journey​

ModuleWeeksFocusKey Skills
Module 1: Foundations1-4ROS 2, Kinematics, ControlBuild basic robot systems
Module 2: Simulation & Perception5-8Gazebo, Isaac Sim, VisionCreate simulated environments
Module 3: Humanoid Robotics9-12Walking, Manipulation, HRIProgram humanoid behaviors
Module 4: AI Integration13-16VLAs, LLMs, DeploymentDeploy intelligent systems

View Full Syllabus →

🛠️ What You'll Build​

Weekly Projects​

  • Week 2: Multi-node robot control system (ROS 2)
  • Week 4: PID-controlled trajectory planner
  • Week 6: Synthetic training dataset (1,000 images)
  • Week 8: Autonomous navigation system with SLAM
  • Week 9: Bipedal walking controller (ZMP-based)
  • Week 11: Whole-body manipulation (walk + grasp)
  • Week 14: Voice-controlled robot (Whisper + LLM + ROS 2)

Capstone Project​

Week 16: Deploy a complete humanoid system that can:

  • Navigate to a specified location
  • Identify and grasp an object
  • Respond to voice commands
  • Operate safely around humans

🚀 Getting Started​

Prerequisites​

  • Programming: Basic Python knowledge (variables, functions, classes)
  • Math: Linear algebra fundamentals (vectors, matrices, transformations)
  • Tools: Familiarity with command line and Git

Hardware Requirements​

  • Minimum: Intel i5/AMD Ryzen 5, 16GB RAM, Ubuntu 22.04
  • Recommended: Intel i7/AMD Ryzen 7, 32GB RAM, RTX 3060+ for simulation

Software Stack​

  • ROS 2: Humble Hawksbill (LTS)
  • Simulation: Gazebo, Isaac Sim, Unity Robotics Hub
  • AI/ML: PyTorch, OpenVLA, OpenAI/Anthropic APIs

📖 Navigation Guide​

Each chapter follows this structure:

  1. Learning Objectives - What you'll achieve
  2. Conceptual Overview - Theory and background
  3. Hands-On Labs - Practical implementation
  4. Code Examples - Working implementations
  5. Assignments - Challenge yourself
  6. Further Reading - Deep dive resources

💡 Pro Tip: Follow the course sequentially. Each week builds on previous concepts.

Next: Start with Chapter 1: Introduction to Physical AI