Semester Course Template: Physical AI & Humanoid Robotics

Overview

This semester course template provides a structured curriculum for teaching Physical AI and humanoid robotics concepts using the content from this book. The template is designed to accommodate different course lengths, student backgrounds, and institutional requirements while maintaining the core learning objectives of the Physical AI curriculum.

Course Information

Basic Course Details

Course Title: Physical AI & Humanoid Robotics
Course Code: [Institution Specific]
Credits: 3-4 credit hours
Duration: 14-16 weeks (standard semester)
Prerequisites: Basic programming, introductory robotics or AI
Corequisites: [As required by institution]

Course Format Options

In-Person: Traditional classroom and lab format
Hybrid: Combination of online and in-person sessions
Online: Fully online with virtual labs and simulations
Flipped Classroom: Online content with in-person labs

Learning Objectives

Primary Learning Outcomes

By the end of this course, students will be able to:

Understand the fundamental concepts of Physical AI and embodied intelligence
Explain the integration of ROS 2, simulation, AI control, and VLA systems
Design and implement basic humanoid robot control systems
Integrate perception, planning, and action in robotic systems
Evaluate the safety and ethical implications of autonomous humanoid systems

Secondary Learning Outcomes

Students will also develop:

Technical skills in robotics software development
Understanding of multimodal AI systems
Experience with simulation-to-reality transfer
Collaborative problem-solving abilities
Technical communication skills

Weekly Schedule Template

Module 1: The Robotic Nervous System (Weeks 1-3)

Week 1: Introduction to Physical AI and ROS 2

Day 1: Course introduction, Physical AI concepts
Day 2: ROS 2 architecture and middleware concepts
Day 3: ROS 2 installation and basic setup
Lab: ROS 2 workspace setup and basic publisher/subscriber
Reading: Book chapters 1 and Module 1 Introduction

Week 2: ROS 2 Communication Patterns

Day 1: Topics, services, and actions theory
Day 2: Advanced ROS 2 concepts and tools
Day 3: ROS 2 best practices and debugging
Lab: Implementing publisher/subscriber for robot data
Reading: Module 1 sections 1-2

Week 3: ROS 2 in Practice

Day 1: URDF and robot description
Day 2: Robot state publisher and TF
Day 3: Review and preparation for Module 1 assessment
Lab: Creating URDF for simple robot model
Reading: Module 1 sections 3-4

Module 2: The Digital Twin (Weeks 4-6)

Week 4: Simulation Fundamentals

Day 1: Physics simulation principles
Day 2: Gazebo vs Unity comparison
Day 3: Introduction to Isaac Sim
Lab: Basic Gazebo simulation setup
Reading: Module 2 Introduction and Principles

Week 5: Sensor Simulation

Day 1: Camera and LiDAR simulation
Day 2: IMU and other sensor simulation
Day 3: Multi-sensor fusion in simulation
Lab: Implementing simulated sensors in Gazebo
Reading: Module 2 sections 3-4

Week 6: Advanced Simulation

Day 1: Domain randomization and synthetic data
Day 2: Simulation-to-reality transfer
Day 3: Review and Module 2 assessment
Lab: Creating synthetic dataset with domain randomization
Reading: Module 2 Learning Outcomes

Module 3: The AI-Robot Brain (Weeks 7-10)

Week 7: Isaac Sim and Perception

Day 1: Isaac Sim architecture and capabilities
Day 2: Isaac ROS introduction
Day 3: GPU-accelerated perception
Lab: Setting up Isaac Sim environment
Reading: Module 3 Introduction and Isaac Sim concepts

Week 8: Isaac ROS Integration

Day 1: Isaac ROS packages overview
Day 2: Visual SLAM with Isaac ROS
Day 3: Navigation systems with Isaac ROS
Lab: Implementing Isaac ROS navigation stack
Reading: Module 3 Isaac ROS integration

Week 9: Bipedal Locomotion

Day 1: Bipedal locomotion fundamentals
Day 2: Balance control and gait generation
Day 3: AI-driven locomotion planning
Lab: Implementing basic walking pattern
Reading: Module 3 Locomotion concepts

Week 10: Advanced AI Control

Day 1: Reinforcement learning for locomotion
Day 2: Whole-body control strategies
Day 3: Review and Module 3 assessment
Lab: Training simple locomotion policy
Reading: Module 3 Learning Outcomes

Module 4: Vision-Language-Action (Weeks 11-13)

Week 11: VLA Fundamentals

Day 1: Vision-Language-Action architecture
Day 2: Multimodal AI integration
Day 3: Introduction to LLMs in robotics
Lab: Setting up VLA development environment
Reading: Module 4 Introduction and VLA concepts

Week 12: Voice-to-Action Systems

Day 1: Speech recognition and understanding
Day 2: Natural language processing for robotics
Day 3: Voice command processing pipeline
Lab: Implementing voice command processing
Reading: Module 4 Voice-to-Action concepts

Week 13: LLM Cognitive Planning

Day 1: LLM integration with ROS 2
Day 2: Cognitive planning with LLMs
Day 3: Review and Module 4 assessment
Lab: Implementing LLM-based task planning
Reading: Module 4 LLM planning

Capstone Integration (Weeks 14-15)

Week 14: Complete System Integration

Day 1: Integrating all modules overview
Day 2: Voice command to perception flow
Day 3: Perception to planning flow
Lab: Integrating voice to planning pipeline
Reading: Capstone Introduction and Voice-Perception flow

Week 15: Complete Pipeline and Testing

Day 1: Planning to navigation to manipulation
Day 2: Complete system testing and validation
Day 3: Final project presentations
Lab: Complete autonomous task execution
Reading: Capstone Implementation Guide and Validation

Week 16: Course Conclusion and Future Directions

Day 1: Course review and key concepts
Day 2: Industry applications and career paths
Day 3: Final exam and course evaluation
Lab: Final project demonstrations
Reading: Conclusion chapters and future directions

Assessment Strategy

Continuous Assessment (70% of grade)

Programming Assignments (30%)

Assignment 1: ROS 2 basics (Week 3) - 10%
Assignment 2: Simulation integration (Week 6) - 10%
Assignment 3: AI control implementation (Week 10) - 10%

Project Work (25%)

Midterm Project: Module integration (Week 8) - 10%
Final Project: Complete system implementation (Week 15) - 15%

Lab Reports (15%)

Weekly Lab Reports: 11 reports at 1.36% each
Focus: Technical implementation, challenges, and solutions

Examinations (30% of grade)

Midterm Exam (15%)

Format: Written exam covering Modules 1-2
Duration: 90 minutes
Focus: Concepts, implementation challenges, and integration

Final Exam (15%)

Format: Written exam covering Modules 3-4 and Capstone
Duration: 120 minutes
Focus: Advanced concepts, system integration, and applications

Laboratory Structure

Lab Session Format (2-3 hours per week)

Pre-lab Discussion (15 minutes): Theory and objectives
Hands-on Implementation (75-90 minutes): Practical work
Results Discussion (15-30 minutes): Review and troubleshooting

Lab Equipment Requirements

Computers: High-performance workstations with GPUs
Robot Platforms: At least 1 robot per 3-4 students
Sensors: Cameras, IMUs, and other relevant sensors
Networking: Reliable network infrastructure

Virtual Lab Options

Simulation Environment: Isaac Sim or Gazebo access
Cloud Computing: GPU instances for heavy computation
Remote Access: Remote lab equipment access

Project Structure

Midterm Project (Week 8)

Objective: Integrate ROS 2 and simulation components
Deliverables:
- Functional ROS 2 node for robot control
- Gazebo simulation with basic navigation
- Lab report documenting implementation
Timeline: 3 weeks for completion

Final Project (Week 15)

Objective: Implement complete autonomous humanoid system
Deliverables:
- Voice command to manipulation pipeline
- Integration of all four modules
- Comprehensive testing and validation
- Final presentation and documentation
Timeline: 5 weeks for completion

Accommodation for Different Backgrounds

For Students with Robotics Background

Advanced Track: Skip basic ROS 2 introduction
Extended Projects: More complex implementation requirements
Research Component: Independent research project option

For Students without Robotics Background

Prerequisites: Additional foundational material
Support Sessions: Extra help sessions for basic concepts
Simplified Projects: Reduced complexity requirements

For Students with AI Background

Focus Shift: Emphasis on AI integration aspects
Advanced AI Projects: More sophisticated AI implementations
Research Projects: AI-focused research projects

Technology Requirements

Software Stack

ROS 2: Humble Hawksbill or later
Isaac ROS: Latest stable release
Isaac Sim: Latest version
Development Tools: VS Code, Git, Docker
AI Frameworks: PyTorch, TensorFlow, Transformers

Hardware Requirements

Development: High-performance workstations with GPUs
Simulation: Systems capable of real-time simulation
Robot Hardware: Humanoid or similar robot platform
Sensors: Complete sensor suite for robot platform

Assessment Rubrics

Programming Assignment Rubric

Functionality (40%): Code works as specified
Code Quality (25%): Clean, well-documented, efficient code
Problem Solving (20%): Appropriate approach to problem
Testing (15%): Adequate testing and validation

Project Rubric

Technical Implementation (40%): Correct technical implementation
Integration (25%): Proper integration of components
Documentation (20%): Clear documentation and explanations
Presentation (15%): Clear presentation of work

Accommodation and Support

Learning Support

Office Hours: Regular instructor and TA office hours
Peer Support: Study groups and peer mentoring
Online Resources: Video tutorials and documentation
Flexible Deadlines: Accommodation for special circumstances

Accessibility

Materials: All materials available in accessible formats
Technology: Assistive technology accommodation
Communication: Multiple communication channels
Assessment: Alternative assessment options when needed

Course Evaluation and Improvement

Student Feedback

Weekly Check-ins: Brief feedback on weekly progress
Mid-semester Survey: Course improvement feedback
Final Evaluation: Comprehensive course evaluation
Focus Groups: In-depth feedback sessions

Continuous Improvement

Curriculum Updates: Regular content updates based on technology
Lab Improvements: Ongoing lab equipment and process improvements
Assessment Refinement: Continuous assessment method refinement
Industry Input: Regular input from industry partners

This semester course template provides a comprehensive framework for teaching Physical AI and humanoid robotics concepts. The template can be adapted based on specific institutional requirements, student backgrounds, and available resources. The next section will provide course adaptation guidelines for different educational contexts.