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Part 1: Foundations of Physical AI

Welcome to Part 1 of the Physical AI and Humanoid Robotics textbook! This foundational section establishes the conceptual and technical groundwork for your journey into embodied artificial intelligence.

The Digital-to-Physical Transition​

For decades, AI has thrived in purely digital environments—mastering chess, translating languages, generating images, and powering recommendation systems. But a profound transformation is underway: AI is breaking free from screens and entering the physical world.

This isn't just about making robots smarter. It's about fundamentally rethinking what intelligence means when it must interact with:

  • Uncertain sensors that provide noisy, incomplete data
  • Physical actuators with latency, limited precision, and safety constraints
  • Real-time deadlines where computation must complete before the robot crashes
  • Unpredictable environments where the coffee mug moves, the floor is slippery, and humans walk into the workspace

Part 1 prepares you to understand and navigate these challenges.

What You'll Learn in Part 1​

Chapter 1: Introduction to Physical AI​

You'll understand why Physical AI requires fundamentally different approaches than software-only systems. We'll explore the robotics revolution, define embodied intelligence, survey real-world applications, and map your 13-week learning path.

Key Questions:

  • What makes physical AI fundamentally different from digital AI?
  • Who are the major players driving the robotics revolution?
  • How do robots perceive, reason about, and act in the real world?
  • Where is Physical AI being deployed today?

Chapter 2: AI Fundamentals Review​

Whether you're coming from software engineering, traditional AI/ML, or starting fresh, this chapter ensures everyone has the necessary AI foundations. We'll review machine learning basics, neural networks, computer vision, NLP essentials, and reinforcement learning—all contextualized for robotics applications.

Key Questions:

  • How do supervised, unsupervised, and reinforcement learning apply to robotics?
  • What neural network architectures power modern robot perception?
  • How do robots "see" and "understand" their environment?
  • How can robots learn from experience?

Learning Approach​

Part 1 is conceptual and strategic, not hands-on code-heavy. You won't install ROS 2 or write robot controllers yet—that comes in Part 2. Instead, you'll build the mental models that make everything else make sense.

Think of Part 1 as the foundation of a building. Without it, subsequent learning becomes fragile and confusing. With it, advanced topics in motion planning, perception, and control will feel like natural extensions of core principles.

  1. Read actively, not passively — Pause to think through thought experiments, sketch diagrams, and connect concepts to your existing knowledge
  2. Don't rush — These foundational concepts compound throughout the course
  3. Engage with interactive elements — Click on collapsible sections, work through exercises, explore further reading
  4. Take notes — Summarize key concepts in your own words

Prerequisites​

  • Curiosity about how AI systems interact with the physical world
  • Basic programming familiarity (Python preferred, but any language works)
  • Mathematical comfort with algebra, geometry, and basic calculus (we'll review as needed)
  • Open mindset toward interdisciplinary thinking (AI + physics + engineering + philosophy)

No prior robotics experience required!

Estimated Time​

⏱️ Total Time for Part 1: 12-16 hours

  • Core reading: 8-10 hours (10 lessons × 50-60 minutes each)
  • Interactive exercises: 2-3 hours
  • Further reading and exploration: 2-3 hours

Part Structure​

Chapter 1: Introduction to Physical AI (5 lessons)

  • From Digital to Physical AI
  • The Robotics Revolution: Players & Technologies
  • Embodied Intelligence: Philosophy & Implementation
  • Applications: From Factories to Homes
  • Your Learning Path: 13-Week Roadmap

Chapter 2: AI Fundamentals Review (5 lessons)

  • Machine Learning Basics
  • Neural Networks Refresher
  • Computer Vision Fundamentals
  • Natural Language Processing Basics
  • Reinforcement Learning Introduction

What Comes Next​

After completing Part 1, you'll move to Part 2: ROS 2 Ecosystem, where theory transforms into practice. You'll install ROS 2 Humble, write your first robot nodes, and begin building the technical skills that power modern robotics systems.

But first, let's build the foundation.

Ready to begin? Start with Chapter 1: Introduction to Physical AI