Skip to main content

Welcome to Physical AI & Humanoid Robotics

Welcome to the most comprehensive, hands-on textbook for mastering Physical AI and Humanoid Robotics. Whether you're a computer science student, a self-learner transitioning into robotics, or a bootcamp attendee aiming for industry readiness, this book will take you from foundational concepts to building complete autonomous humanoid systems.

Course Philosophy: Why Physical AI Matters​

We are witnessing a pivotal moment in technology history. Artificial Intelligence, which has revolutionized software and digital experiences, is now breaking free from the constraints of screens and servers. Physical AI—the embodiment of intelligence in machines that perceive, reason, and act in the real world—is the next frontier.

Humanoid robots are no longer science fiction. Companies like Tesla (Optimus), Boston Dynamics (Atlas), Figure AI, Agility Robotics (Digit), and 1X Technologies are racing to build general-purpose humanoid platforms that can work alongside humans in factories, warehouses, homes, and beyond. This textbook prepares you to contribute to this future.

Unlike traditional robotics courses that focus narrowly on manipulation or navigation, this textbook teaches the complete stack: from AI fundamentals and ROS 2 architecture through perception, planning, control, and human-robot interaction. You'll learn not just to use libraries, but to implement core algorithms from scratch and understand the tradeoffs that matter in production systems.

Who This Book Is For​

This textbook is designed for:

  • Computer Science Students: Seeking a rigorous, graduation-level robotics course comparable to offerings at MIT, CMU, or Stanford
  • Self-Learners: Transitioning from software engineering or AI into robotics and needing structured, comprehensive training
  • Bootcamp Attendees: Looking for hands-on, project-based learning with immediate applicability to industry roles
  • Researchers & Engineers: Wanting to deepen their understanding of humanoid robotics systems and modern Physical AI techniques

What makes this book unique:

  • Graduation-level rigor: Implement algorithms from research papers, derive mathematical formulations, analyze complexity
  • Modern technology stack: ROS 2 Humble, NVIDIA Isaac Sim, Python 3.10+, cutting-edge perception and planning tools
  • Production focus: Professional code quality, error handling, logging, testing, and deployment practices
  • Complete integration: From first principles through full autonomous system development

Prerequisites​

Before beginning this course, you should have:

Required Knowledge:

  • Python Programming: Intermediate proficiency (classes, decorators, type hints, async/await)
  • Linear Algebra: Vectors, matrices, transformations, eigenvalues (review provided in early lessons)
  • Calculus: Derivatives, gradients, optimization basics
  • Command-Line Proficiency: Bash/shell scripting, package management, environment variables

Recommended (but not required):

  • Basic machine learning concepts (supervised learning, neural networks)
  • C++ fundamentals (helpful for ROS 2 advanced topics, but Python-first approach)
  • Control theory basics (PID, state-space models)

Hardware & Software Requirements:

  • Minimum: Laptop with Ubuntu 22.04 LTS (virtual machine acceptable), 8GB RAM, GPU not required for simulation
  • Recommended: Workstation with NVIDIA GPU (RTX 3060+), 16GB+ RAM, for Isaac Sim and real-time perception
  • Optional: Jetson Orin Nano or robotics hardware for physical deployment (covered in appendices)

See Hardware Recommendations and Installation Guide for detailed setup instructions.

How to Use This Book​

This textbook is structured for progressive mastery over 13 weeks of intensive study (10-15 hours per week). However, you can adapt the pace to your schedule.

Learning Strategies:

For Students (University Course):

  • Follow the 13-week schedule sequentially
  • Complete all hands-on exercises and review questions
  • Work on the capstone project in parallel starting Week 8
  • Form study groups to discuss tradeoffs and debug implementations

For Self-Learners (Flexible Pace):

  • Budget 2-3 hours per lesson (reading + exercises + exploration)
  • Build small projects at the end of each Part to consolidate knowledge
  • Join our GitHub Discussions community for support
  • Focus on Parts 1-5 for core competency, then specialize in areas of interest

For Bootcamp Students (Accelerated Track):

  • Prioritize hands-on exercises over deep theory (revisit derivations later)
  • Pair program on complex implementations
  • Attend office hours or Q&A sessions for debugging
  • Build portfolio projects showcasing work from each Part

Lesson Structure: Each lesson follows a consistent 8-section format designed for effective learning:

  1. Learning Objectives: Clear, measurable outcomes
  2. Introduction: Context and motivation
  3. Main Content: Theory, algorithms, and analysis (3-5 major sections)
  4. Hands-On Practice: Guided exercises with solution approaches
  5. Key Takeaways: Essential points to remember
  6. Review Questions: Self-assessment with answers
  7. Further Reading: Curated resources for deeper exploration
  8. What's Next: Navigation to the next lesson with context

13-Week Learning Path Overview​

Part 1: Foundations of Physical AI (Weeks 1-2)​

Build your conceptual foundation. Understand what makes Physical AI different from software-only AI, master coordinate systems and transformations, and review essential AI fundamentals. Key Outcome: Mental model of embodied intelligence and mathematical toolkit for robotics.

Part 2: ROS 2 Ecosystem (Weeks 3-5)​

Master the Robot Operating System 2 (ROS 2), the industry-standard middleware for robot software. Learn nodes, topics, services, actions, TF2 transforms, and URDF robot modeling. Key Outcome: Ability to architect and implement distributed robot systems using ROS 2 Humble.

Part 3: Simulation Environments (Weeks 5-7)​

Work with Gazebo Classic/Garden, Unity Robotics Hub, and NVIDIA Isaac Sim to prototype and test robots in high-fidelity physics simulations. Key Outcome: Simulation-first development workflow and understanding of sim-to-real transfer challenges.

Part 4: NVIDIA Isaac Platform (Weeks 7-9)​

Leverage GPU-accelerated perception, planning, and manipulation using NVIDIA Isaac Sim and Isaac ROS. Master hardware-accelerated SLAM, object detection, motion generation, and navigation. Key Outcome: Production-grade perception pipelines and real-time performance optimization.

Part 5: Humanoid Development (Weeks 9-10)​

Dive into humanoid-specific challenges: balance and stability (ZMP, capture point), inverse kinematics, whole-body control, and gait generation. Implement bipedal walking controllers. Key Outcome: Understanding of humanoid dynamics and locomotion algorithms.

Part 6: Conversational Robotics (Week 11)​

Build multi-modal human-robot interaction systems using speech recognition, natural language understanding, vision-language models, and gesture recognition. Key Outcome: Integration of LLMs and VLMs into robot control pipelines.

Part 7: Capstone Project (Weeks 12-13)​

Design, implement, and deploy a complete autonomous humanoid system integrating perception, planning, manipulation, navigation, and interaction. Key Outcome: Portfolio-ready capstone demonstrating end-to-end robotics competency.

Appendices: Reference Materials​

Comprehensive guides on hardware selection, installation procedures, troubleshooting, curated resources, and technical glossary.

Community & Support​

Learning robotics is challenging—you don't have to do it alone.

Community Resources:

  • GitHub Discussions: Ask questions, share projects, collaborate with peers
    🔗 Join Discussions
  • Issue Tracker: Report errors, suggest improvements, request clarifications
    🔗 Report Issues
  • Contributions: Help improve content, add translations, or contribute exercises
    📄 See CONTRIBUTING.md

External Communities:

Getting Started​

Ready to begin your journey into Physical AI and Humanoid Robotics?

Next Steps:

  1. Set up your development environment → Installation Guide
  2. Start with Part 1, Chapter 1 → Introduction to Physical AI
  3. Bookmark key resources → Further Reading
  4. Join the community → GitHub Discussions

Let's build the future of Physical AI together. 🚀

Begin Part 1: Foundations of Physical AI →