13-Week Course Breakdown
This document provides a detailed breakdown of the Physical AI & Humanoid Robotics course for a 13-week semester.
Course Structure
The course is organized into 5 modules with the following distribution:
| Module | Weeks | Hours | Weight |
|---|---|---|---|
| Module 1: Introduction to Physical AI | 1-2 | 6 | 15% |
| Module 2: ROS 2 Fundamentals | 3-5 | 9 | 23% |
| Module 3: Simulation Environments | 6-7 | 6 | 15% |
| Module 4: NVIDIA Isaac Platform | 8-10 | 9 | 23% |
| Module 5: Vision-Language-Action Systems | 11-13 | 9 | 24% |
Week-by-Week Schedule
Week 1: Physical AI Foundations
Topics:
- What is Physical AI?
- Sensor systems overview (cameras, LiDAR, IMUs)
Learning Objectives:
- Define Physical AI and explain how it differs from traditional AI
- Describe the key challenges of embodied intelligence
- Identify the core components of a Physical AI system
Assignments:
- Reading: Chapter 1.1-1.2
- Exercise: Sensor comparison worksheet
- Lab: None (foundational week)
Preparation Notes:
- Ensure students have Python environment set up
- Recommend installing ROS 2 Humble for next week
Week 2: Embodied Intelligence & Humanoid Design
Topics:
- Embodied cognition principles
- Humanoid robot advantages
- Sensorimotor loops
Learning Objectives:
- Explain the concept of embodied cognition
- Describe how physical interaction shapes learning
- Understand the advantages of humanoid form factors
Assignments:
- Reading: Chapter 1.3-1.4
- Exercise: Sensorimotor loop implementation
- Lab: Simple sensor simulation in Python
Week 3: ROS 2 Introduction
Topics:
- ROS 2 architecture overview
- Nodes and executors
- Topics and messages
Learning Objectives:
- Understand ROS 2 architecture and design philosophy
- Create and run ROS 2 nodes in Python
- Publish and subscribe to topics
Assignments:
- Reading: Chapter 2.1
- Exercise: Create a simple publisher/subscriber pair
- Lab: ROS 2 installation and "Hello World" node
Week 4: ROS 2 Communication Patterns
Topics:
- Services (synchronous RPC)
- Actions (long-running tasks)
- Parameters
Learning Objectives:
- Implement ROS 2 services and clients
- Create action servers for long-running tasks
- Use parameter servers for configuration
Assignments:
- Reading: Chapter 2.2
- Exercise: Service-based calculator node
- Lab: Action server for waypoint navigation
Week 5: Robot Description
Topics:
- URDF (Unified Robot Description Format)
- TF2 coordinate transforms
- Launch files
Learning Objectives:
- Create URDF descriptions for humanoid robots
- Understand and use TF2 for coordinate transforms
- Write launch files for multi-node systems
Assignments:
- Reading: Chapter 2.3-2.4
- Exercise: URDF for simple humanoid arm
- Lab: Visualize robot in RViz2
Week 6: Gazebo Simulation Basics
Topics:
- Gazebo architecture
- Physics simulation
- World creation
Learning Objectives:
- Set up Gazebo simulation environments
- Configure physics parameters
- Create custom worlds
Assignments:
- Reading: Chapter 3.1
- Exercise: Gazebo world with obstacles
- Lab: Import robot model into Gazebo
Week 7: Advanced Simulation
Topics:
- Sensor plugins
- Unity rendering integration
- Simulation best practices
Learning Objectives:
- Add sensor plugins to simulated robots
- Understand photo-realistic rendering approaches
- Design effective simulation experiments
Assignments:
- Reading: Chapter 3.2-3.3
- Exercise: Custom sensor plugin
- Lab: Complete simulation environment
Midterm Project Assigned: Navigation in simulated environment
Week 8: NVIDIA Isaac Sim Introduction
Topics:
- Isaac Sim architecture
- NVIDIA Omniverse basics
- USD (Universal Scene Description)
Learning Objectives:
- Navigate Isaac Sim interface
- Understand USD format for scene description
- Set up Isaac Sim for humanoid simulation
Assignments:
- Reading: Chapter 4.1
- Exercise: Isaac Sim tutorial walkthrough
- Lab: Import humanoid into Isaac Sim
Week 9: Isaac ROS Integration
Topics:
- Isaac ROS packages
- Visual SLAM with Isaac
- Perception pipelines
Learning Objectives:
- Connect Isaac Sim to ROS 2
- Implement Visual SLAM with Isaac ROS
- Build perception pipelines
Assignments:
- Reading: Chapter 4.2
- Exercise: VSLAM configuration
- Lab: Real-time perception in Isaac Sim
Midterm Project Due
Week 10: Navigation and Locomotion
Topics:
- Nav2 for path planning
- Bipedal locomotion control
- Obstacle avoidance
Learning Objectives:
- Configure Nav2 for humanoid navigation
- Understand bipedal gait generation
- Implement reactive obstacle avoidance
Assignments:
- Reading: Chapter 4.3-4.4
- Exercise: Nav2 configuration for humanoid
- Lab: Autonomous navigation demo
Week 11: Vision-Language-Action Systems
Topics:
- VLA architecture overview
- Voice-to-action pipelines
- Speech recognition integration
Learning Objectives:
- Understand VLA system components
- Build voice command interfaces
- Connect speech to robot actions
Assignments:
- Reading: Chapter 5.1
- Exercise: Voice command processing
- Lab: Speech-controlled robot movements
Capstone Project Assigned
Week 12: LLM-Based Planning
Topics:
- LLM cognitive architectures
- Task planning with language models
- Prompt engineering for robotics
Learning Objectives:
- Integrate LLMs for task decomposition
- Design prompts for robotic planning
- Handle uncertainty in LLM outputs
Assignments:
- Reading: Chapter 5.2
- Exercise: LLM planner implementation
- Lab: Natural language task execution
Week 13: Capstone Presentations
Topics:
- Project presentations
- Course review
- Future directions in Physical AI
Activities:
- Student capstone presentations (15 min each)
- Peer feedback and discussion
- Course wrap-up and industry outlook
Capstone Project Due
Assessment Calendar
| Week | Assessment |
|---|---|
| 2 | Lab 1 Due |
| 5 | Lab 2 Due |
| 7 | Midterm Project Assigned |
| 7 | Lab 3 Due |
| 9 | Midterm Project Due |
| 10 | Lab 4 Due |
| 11 | Capstone Assigned |
| 12 | Lab 5 Due |
| 13 | Capstone Due + Presentations |
Recommended Office Hours Topics
- Week 1-2: Python environment setup, conceptual questions
- Week 3-5: ROS 2 debugging, node communication issues
- Week 6-7: Gazebo physics tuning, simulation stability
- Week 8-10: Isaac Sim installation, GPU configuration
- Week 11-13: Project guidance, LLM integration
This course breakdown is designed for a semester-long course with 3 hours of instruction per week plus additional lab time.