Introduction to Physical AI
Welcome to the Physical AI & Humanoid Robotics curriculum, a comprehensive 13-week journey into building intelligent humanoid robots with conversational AI capabilities.
What is Physical AI?
Physical AI represents the convergence of artificial intelligence with physical systems, enabling robots to:
- Perceive their environment through vision, audio, and tactile sensors
- Reason about the world using language models and planning algorithms
- Act in the physical world through precise motor control and locomotion
- Learn from experience and adapt to new situations
Curriculum Overview
This curriculum is structured into 13 weeks across 5 modules:
Foundation (Weeks 1-2)
| Week | Topic | Description |
|---|---|---|
| 1 | Introduction to Physical AI | Concepts, hardware requirements, setup |
| 2 | Physical AI Architecture | System design, integration patterns |
Module 1: ROS 2 Fundamentals (Weeks 3-5)
| Week | Topic | Description |
|---|---|---|
| 3 | Introduction to ROS 2 | Installation, workspace, CLI tools |
| 4 | ROS 2 Nodes and Topics | Publish/subscribe communication |
| 5 | ROS 2 Services and Actions | Synchronous and asynchronous communication |
Module 2: Robot Simulation - Gazebo (Weeks 6-7)
| Week | Topic | Description |
|---|---|---|
| 6 | Introduction to Gazebo | Simulation setup, world creation |
| 7 | Robot Modeling in Gazebo | URDF, sensors, ROS 2 control |
Module 3: NVIDIA Isaac Platform (Weeks 8-10)
| Week | Topic | Description |
|---|---|---|
| 8 | Introduction to Isaac Sim | Omniverse, USD, GPU simulation |
| 9 | Isaac ROS Integration | ROS 2 bridge, control, navigation |
| 10 | Perception Models | Vision, depth, sensor fusion |
Module 4: Humanoid Development & Conversational AI (Weeks 11-13)
| Week | Topic | Description |
|---|---|---|
| 11 | Humanoid Robot Basics | Kinematics, dynamics, balance control |
| 12 | Conversational AI Integration | LLMs, VLA models, voice interface |
| 13 | Complete Humanoid System | Integration, deployment, capstone |
Hardware Requirements
Development System
| Component | Minimum | Recommended |
|---|---|---|
| GPU | NVIDIA RTX 3060 (12GB) | NVIDIA RTX 4090 (24GB) |
| CPU | Intel i7 / AMD Ryzen 7 (8 cores) | Intel i9 / AMD Ryzen 9 (16 cores) |
| RAM | 32GB DDR4 | 64GB DDR5 |
| Storage | 500GB NVMe SSD | 1TB NVMe SSD |
| OS | Ubuntu 22.04 LTS | Ubuntu 22.04 LTS |
Edge Deployment
| Component | Specification | Purpose |
|---|---|---|
| Edge AI | NVIDIA Jetson Orin Nano / Orin AGX | On-robot inference |
| Sensors | RGB-D cameras, LiDAR, IMU | Perception stack |
| Actuators | Dynamixel servos, BLDC motors | Robot actuation |
Prerequisites
Before starting this curriculum, you should have:
- Programming: Proficiency in Python and basic C++
- Linux: Comfort with Ubuntu command line
- Mathematics: Linear algebra, calculus, and probability
- Robotics: Basic understanding of kinematics (helpful but not required)
If you need to brush up on prerequisites, see the Prerequisites page.
Getting Started
- Verify Hardware: Ensure your system meets the minimum requirements
- Install Software: Follow the setup instructions in Week 1
- Start Learning: Begin with Week 1 - Introduction to Physical AI
Learning Approach
This curriculum emphasizes hands-on learning:
- Code Examples: Every concept includes working code
- Exercises: Practical tasks to reinforce learning
- Projects: Build toward a complete humanoid system
- Integration: Connect perception, reasoning, and action
Community and Support
- Documentation: Each week includes detailed documentation
- Code Repository: All examples available on GitHub
- Discussion: Join the community forum for questions
After Completion
Upon completing this curriculum, you will be able to:
- Design and implement ROS 2-based robot systems
- Create digital twins and simulate robots in Gazebo and Isaac Sim
- Deploy perception models for environmental understanding
- Integrate language models for natural human-robot interaction
- Build complete humanoid robot systems
Let's begin your journey into Physical AI!