Introduction to the Physical AI & Robotics Course
This book covers the complete journey of learning Physical AI, Robotics, Simulation, Perception, and Robot Intelligence using modern tools like ROS 2, Gazebo, Unity, and NVIDIA Isaac. The curriculum is organized week-by-week to help you build strong foundations and gradually move toward advanced humanoid robotics.
🧠 Weeks 1–2: Introduction to Physical AI
Foundations of Physical AI and Embodied Intelligence
Learn how AI moves from digital systems to physical machines that interact with the real world.
From Digital AI to Robots That Understand Physical Laws
Explore how robots understand physics, forces, motion, and real-world constraints.
Overview of the Humanoid Robotics Landscape
Discover global humanoid robots like Tesla Optimus, Figure 01, Agility Digit, Unitree H1, and more.
Sensor Systems
- LiDAR
- Cameras
- IMUs
- Force/Torque Sensors
These sensors help robots perceive the environment with accuracy.
🤖 Weeks 3–5: ROS 2 Fundamentals
ROS 2 Architecture and Core Concepts
Understand the middleware powering modern robots.
Nodes, Topics, Services, and Actions
Learn asynchronous communication systems in robotics.
Building ROS 2 Packages with Python
Develop modules and robotics software using rclpy.
Launch Files & Parameter Management
Automate your robot systems with configurable launches.
🏗️ Weeks 6–7: Robot Simulation with Gazebo
Gazebo Simulation Environment Setup
Install and configure Gazebo for physics-based robotics.
URDF & SDF Robot Description Formats
Model the physical structure of robots.
Physics Simulation & Sensor Simulation
Simulate gravitational forces, collisions, joints, and sensors.
Introduction to Unity for Robot Visualization
Learn how Unity can create high-quality interactive 3D robot scenes.
⚡ Weeks 8–10: NVIDIA Isaac Platform
NVIDIA Isaac SDK & Isaac Sim
Use GPU-accelerated tools to develop, test, and deploy intelligent robots.
AI-Powered Perception and Manipulation
Teach robots to detect objects, segment environments, and grasp.
Reinforcement Learning for Robot Control
Train robots using reward-based AI systems.
Sim-to-Real Transfer Techniques
Move trained robot models from simulation to real hardware.
🦾 Weeks 11–12: Humanoid Robot Development
Humanoid Robot Kinematics & Dynamics
Build models for bipedal walking, movement, and control.
Bipedal Locomotion & Balance Control
Use control algorithms to stabilize humanoid robots.
Manipulation & Grasping
Teach humanoids to use hands for real-world tasks.
Natural Human–Robot Interaction (HRI)
Speech, gestures, safety rules, and intuitive robot interfaces.
🗣️ Week 13: Conversational Robotics
Integrating GPT Models
Use LLMs to give robots intelligent conversational abilities.
Speech Recognition & Natural Language Understanding
Enable robots to listen, understand, and respond.
Multimodal Interaction (Speech, Gesture, Vision)
Combine voice, visual input, and gestures for real interaction.
🔀 Overall Course Workflow Diagram
+---------------------------+
| Weeks 1–2: Physical AI |
| Foundations & Sensors |
+--------------+------------+
|
v
+---------------------------+
| Weeks 3–5: ROS 2 |
| Communication & Control |
+--------------+------------+
|
v
+---------------------------+
| Weeks 6–7: Gazebo Sim |
| Robot Modeling & Physics |
+--------------+------------+
|
v
+---------------------------+
| Weeks 8–10: Isaac Platform |
| AI Perception & RL |
+--------------+------------+
|
v
+---------------------------+
| Weeks 11–12: Humanoids |
| Locomotion & Grasping |
+--------------+------------+
|
v
+---------------------------+
| Week 13: Conversational AI |
| GPT + Speech + Vision |
+---------------------------+
Agar chaho to main Chapter 1: Foundations of Physical AI ka full detailed chapter (with diagrams + examples + MCQs) bhi create kar dun.