CH1 – Physical AI
Foundations of Physical AI & Embodied Intelligence
High-level course overview, motivations for Physical AI, embodied intelligence, and the three-pillar framework.
📘Technical Guide • 2025 Edition
Physical AI & Humanoid Robotics
— An AI‑Native, Spec‑Driven Book
A course-aligned, AI-native book on embodied intelligence and humanoid robotics. Every chapter, lab, and feature is traceable to specs, tests, and research in this repository—from ROS 2 and digital twins to ethical governance and conversational robots.
# Spec → Docs → Labs → Robot
class PhysicalAIBook:
def specs():
# /specs/*.md: course, book, chatbot
def chapters():
# /docs/01-intro, /docs/chapters/*
def labs():
# /docs/labs: ROS 2 & simulation
def chatbot():
# RAG over the entire bookSpec-Driven Development
Learn industry-standard SDD approaches for building robust, maintainable AI systems with clear specifications and test-driven development.
Embodied Intelligence
Master the Perception-Decision-Action loop and understand how AI systems interact with the physical world through sensors and actuators.
Hands-On Projects
Build real-world applications with ROS 2, Gazebo simulation, and modern AI tools. Includes practical examples and code samples.
Three Pillars Framework
Understand the foundational framework: Simulation, Perception & Control, and Embodied AI that powers modern Physical AI systems.
How This AI-Native Book Is Structured
The landing page reflects the underlying specs, chapters, labs, and AI features defined in this repository. Use the filters to explore the different layers of the project.
CH2 – Physical AI Foundations
Robotic Nervous System & PDA Loop
Deep dive into ROS 2 as the robot nervous system plus the perception–decision–action loop.
CH3 – The Digital Twin
Simulation-First Robotics & Digital Twins
Build and reason about high-fidelity digital twins before touching real hardware.
CH4 – Ethics & Governance
Responsible Physical AI & Governance
Ethical design, bias mitigation, transparency, safety, and governance for embodied AI systems.
Labs – ROS 2 & Simulation
Hands-on Labs: ROS 2 & Simulation
Guided labs for ROS 2 fundamentals, node communication, package creation, and simulation environments.
Project – Book Architecture
Physical AI & Humanoid Robotics Book Spec
Master spec defining chapters, evaluation criteria, deployment, and overall architecture of this AI-native book.
Project – AI-Native Bot
AI-Native Bot & Docs Experience
Spec for this Docusaurus + chatbot experience, including RAG pipeline, deployment, and integration plans.
Feature – RAG Chatbot
In-Docs RAG Chatbot
Ask questions about any chapter or lab, get cited answers, and explore the content interactively.
Feature – Personalization
Auth, Profiles & Personalized Views
Design for Better-Auth integration, user profiles, and chapter-level personalization & Urdu translation.
What You'll Learn
A spec-driven journey from foundations of Physical AI to ROS 2, digital twins, and ethical design
Chapter 1
Physical AI & Embodied Intelligence
- Course overview & roadmap
- Foundations of Physical AI
- Embodied intelligence & PDA loop
- Technical stack & platform choices
Chapter 2
The Robotic Nervous System (ROS 2)
- ROS 2 nodes, topics & services
- Robot description (URDF)
- Perception & control pipelines
- Course-aligned ROS 2 labs
Chapter 3
Digital Twins & Simulation
- Gazebo & high-fidelity Isaac Sim
- Physics, sensors & synthetic data
- Sim-to-real transfer
- Lab: simulation & environment setup
Chapter 4
Ethical AI & Responsible Design
- Foundational principles & governance
- Privacy, safety & oversight
- Transparency & explainability
- Course constitution & constraints
What Experts Say
"
A comprehensive guide that bridges the gap between AI theory and practical robotics implementation. Essential reading for anyone entering the field of Physical AI.
Dr. Sarah ChenRobotics Professor, MIT
"
Finally, a book that explains Physical AI in a way that's both technically rigorous and accessible. The hands-on examples are invaluable.
Michael RodriguezSenior AI Engineer, Tesla
"
This guide perfectly captures the convergence of AI and robotics. The spec-driven approach is exactly what the industry needs.
Dr. Aisha PatelResearch Scientist, Boston Dynamics
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