Learn Physical AI

Why This Textbook

Everything You Need to Master Physical AI

A comprehensive learning platform combining textbook content with an AI-powered assistant.

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In-Depth Textbook

20+ technical chapters covering ROS 2, simulation, Isaac Sim, and Vision-Language-Action systems.

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AI Chatbot

RAG-powered assistant trained on textbook content to answer your questions instantly.

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Hands-On Projects

Practical labs and exercises to build real-world Physical AI applications.

Core Curriculum

Structured Learning Roadmap

Master Physical AI through a structured 4-step process designed for engineers.

The Robotic Nervous System (ROS 2)

Understanding ROS 2 fundamentals including nodes, topics, publishers, subscribers, services, and actions for humanoid robots.

Publishers/SubscribersServices & ActionsHumanoid Nodes

The Digital Twin (Gazebo & Unity)

Simulation environments for robotics including URDF/SDF robot descriptions, physics simulation, and sensor simulation techniques.

URDF / SDF ModelingGazebo PhysicsSensor Simulation

The AI-Robot Brain (NVIDIA Isaac™)

AI-powered navigation and perception systems using Isaac Sim, VSLAM navigation, and sim-to-real transfer techniques.

Isaac SimVSLAM NavigationSim-to-Real Transfer

Vision-Language-Action (VLA) Systems

Vision-Language-Action integration for cognitive robots using voice-to-action systems and LLM-based cognitive planning.

OpenAI WhisperVoice-to-ActionLLM Planning

Sim-to-Real Workflow

From Digital Simulation to Physical Hardware

Learn the exact engineering lifecycle used by frontier AI robotics teams.

01Robot Structure Definition

URDF Kinematics

Define joint limits, link dynamics, inertia matrices, and visual mesh files in URDF format.

02Physics Simulation Environment

Gazebo Digital Twin

Simulate friction, gravity, LiDAR point clouds, and motor torque response prior to physical testing.

03GPU-Accelerated Perception

Isaac Sim VSLAM

Generate occupancy grids, depth camera feeds, and real-time visual localization using NVIDIA Isaac ROS.

04Sim-to-Real Motor Control

Physical Humanoid

Deploy zero-shot reinforcement learning policies and ROS 2 action servers to real hardware actuators.

4Core Textbook Modules

20+In-depth Technical Chapters

100%RAG AI Q&A Assistance

Open SourceROS 2 & Isaac Repositories

Creative Director

Next Release

About the Author

Arishba Khan

Creative Technologist & Agentic AI Engineer

I specialize in merging high-end digital design with advanced engineering workflows, building interactive and fluid web experiences through modern front-end tech, custom shaders, and motion design. Beyond core development, I lead initiatives focused on AI workflows in web development and technical systems.

✦Agentic AI Workflows

✦Custom Shaders & Motion

✦Frontend Engineering

GitHub LinkedIn

RAG Intelligence

AI-Powered Learning Assistant

Our RAG (Retrieval-Augmented Generation) chatbot is here to help you understand Physical AI concepts with ease. It has been trained specifically on this textbook content to provide accurate, contextual answers to your questions. Whether you need clarification on complex topics or want to dive deeper into specific subjects, our intelligent assistant can simplify content and guide your learning journey.

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Contextual Understanding

Grasps the context of your questions based on the textbook content

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Content Simplification

Breaks down complex concepts into easy-to-understand explanations

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Direct References

Provides specific references to textbook sections for further reading

Try It Out

Sample Questions for the AI Assistant

Curious what the chatbot can do? Here are some example prompts to get you started.

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ROS 2 Communication

"How do ROS 2 Nodes communicate via Actions?"

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Robot Modeling

"What is the difference between URDF and SDF?"

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Navigation & Perception

"How does VSLAM function inside NVIDIA Isaac Sim?"

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Voice-to-Action

"How to integrate OpenAI Whisper with ROS 2?"