Tutorial Intro
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.
π§ Weeks 1β2: Introduction to Physical AI
Foundations of Physical AI and Embodied Intelligenceβ
Learn how AI moves from digital systems to physical machines that interact with the real world.
From Digital AI to Robots That Understand Physical Lawsβ
Explore how robots understand physics, forces, motion, and real-world constraints.
Overview of the Humanoid Robotics Landscapeβ
Discover global humanoid robots like Tesla Optimus, Figure 01, Agility Digit, Unitree H1, and more.
Sensor Systemsβ
- LiDAR
- Cameras
- IMUs
- Force/Torque Sensors
These sensors help robots perceive the environment with accuracy.
π€ Weeks 3β5: ROS 2 Fundamentals
ROS 2 Architecture and Core Conceptsβ
Understand the middleware powering modern robots.
Nodes, Topics, Services, and Actionsβ
Learn asynchronous communication systems in robotics.
Building ROS 2 Packages with Pythonβ
Develop modules and robotics software using rclpy.
Launch Files & Parameter Managementβ
Automate your robot systems with configurable launches.
ποΈ Weeks 6β7: Robot Simulation with Gazebo
Gazebo Simulation Environment Setupβ
Install and configure Gazebo for physics-based robotics.
URDF & SDF Robot Description Formatsβ
Model the physical structure of robots.
Physics Simulation & Sensor Simulationβ
Simulate gravitational forces, collisions, joints, and sensors.
Introduction to Unity for Robot Visualizationβ
Learn how Unity can create high-quality interactive 3D robot scenes.
β‘ Weeks 8β10: NVIDIA Isaac Platform
NVIDIA Isaac SDK & Isaac Simβ
Use GPU-accelerated tools to develop, test, and deploy intelligent robots.
AI-Powered Perception and Manipulationβ
Teach robots to detect objects, segment environments, and grasp.
Reinforcement Learning for Robot Controlβ
Train robots using reward-based AI systems.
Sim-to-Real Transfer Techniquesβ
Move trained robot models from simulation to real hardware.
π¦Ύ Weeks 11β12: Humanoid Robot Development
Humanoid Robot Kinematics & Dynamicsβ
Build models for bipedal walking, movement, and control.
Bipedal Locomotion & Balance Controlβ
Use control algorithms to stabilize humanoid robots.
Manipulation & Graspingβ
Teach humanoids to use hands for real-world tasks.
Natural HumanβRobot Interaction (HRI)β
Speech, gestures, safety rules, and intuitive robot interfaces.
π£οΈ Week 13: Conversational Robotics
Integrating GPT Modelsβ
Use LLMs to give robots intelligent conversational abilities.
Speech Recognition & Natural Language Understandingβ
Enable robots to listen, understand, and respond.
Multimodal Interaction (Speech, Gesture, Vision)β
Combine voice, visual input, and gestures for real interaction.
π Overall Course Workflow Diagram
+---------------------------+
| Weeks 1β2: Physical AI |
| Foundations & Sensors |
+--------------+------------+
|
v
+---------------------------+
| Weeks 3β5: ROS 2 |
| Communication & Control |
+--------------+------------+
|
v
+---------------------------+
| Weeks 6β7: Gazebo Sim |
| Robot Modeling & Physics |
+--------------+------------+
|
v
+---------------------------+
| Weeks 8β10: Isaac Platform |
| AI Perception & RL |
+--------------+------------+
|
v
+---------------------------+
| Weeks 11β12: 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.