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Instructor Guide - Weekly Lesson Plans

This section provides detailed lesson plans for each week of the Physical AI & Humanoid Robotics course, including learning objectives, activities, assessments, and pedagogical notes.

General Weekly Structure​

Each week typically follows this structure:

  • Day 1: Theory and concepts introduction
  • Day 2: Implementation and hands-on practice
  • Day 3: Lab exercises and project work
  • Day 4: Assessment and troubleshooting
  • Day 5: Project work and advanced topics

Week 1-2: ROS 2 Architecture and Concepts​

Learning Objectives​

  • Students will understand the fundamental architecture of ROS 2
  • Students will create and run basic ROS 2 nodes in Python
  • Students will implement message passing between nodes using topics and services

Day 1: ROS 2 Architecture Overview​

Duration: 2 hours lecture + 1 hour lab prep

Activities:

  1. Lecture (60 min):

    • ROS 2 vs. ROS 1 architectural differences
    • DDS (Data Distribution Service) concept
    • Nodes, topics, services, actions explanation
    • Launch files and parameter management

  2. Lab Setup (30 min):

    • Verify ROS 2 installation on student workstations
    • Basic ROS 2 commands demonstration
    • Workspace setup for the week

  3. Concept Check (30 min):

    • Quick quiz on ROS 2 fundamental concepts
    • Discussion of common misconceptions

Resources Needed:

  • Projector for demonstrations
  • Prepared ROS 2 workspace with example packages
  • Student handout with essential ROS 2 commands

Instructor Notes:

  • Emphasize the distributed architecture concept early
  • Address common confusion between topics and services
  • Prepare troubleshooting guide for common installation issues

Day 2: Creating ROS 2 Nodes​

Duration: 1 hour lecture + 3 hours lab

Activities:

  1. Lecture (30 min):

    • Node structure and lifecycle
    • Publisher and subscriber implementation
    • Parameter servers and configuration

  2. Hands-on Practice (2.5 hours):

    • Students create their first ROS 2 node
    • Implement publisher and subscriber nodes
    • Debug common node creation issues

Lab Exercise:

  • Create a simple talker/listener node pair
  • Extend to broadcast sensor data from a simulated sensor

Assessment:

  • Formative: Instructor observation during lab
  • Students demonstrate successful node communication

Instructor Notes:

  • Be prepared for Python version compatibility issues
  • Have pre-built example nodes available for reference
  • Monitor for common mistakes in node creation patterns

Day 3: Services and Actions​

Duration: 1 hour lecture + 3 hours lab

Activities:

  1. Lecture (30 min):

    • Services vs. topics: when to use each
    • Actions for long-running tasks
    • Error handling and node resilience

  2. Hands-on Practice (2.5 hours):

    • Implement service client and server
    • Create action client and server
    • Integrate services with existing nodes

Lab Exercise:

  • Add a service to control robot movement
  • Implement an action for path planning

Assessment:

  • Students demonstrate working service and action implementations
  • Peer review of code quality and error handling

Instructor Notes:

  • Clarify the difference between services and actions early and often
  • Prepare examples of real-world use cases for each
  • Monitor for issues with asynchronous service calls

Day 4: Launch Files and Parameter Management​

Duration: 1 hour lecture + 2 hours lab

Activities:

  1. Lecture (30 min):

    • Launch file syntax and structure
    • Parameter management best practices
    • Node composition and optimization

  2. Hands-on Practice (1.5 hours):

    • Create launch files for multi-node systems
    • Implement parameter configuration
    • Optimize node performance

Assessment:

  • Quiz on launch file syntax and parameter management
  • Demonstration of properly configured multi-node system

Instructor Notes:

  • Emphasize importance of parameter management in complex systems
  • Prepare examples of well-structured launch files
  • Be ready to debug complex launch file configurations

Day 5: Week Review and Troubleshooting​

Duration: 2 hours

Activities:

  1. Project Work (1 hour):

    • Students work on integrated ROS 2 system
    • Instructors provide individualized support

  2. Troubleshooting Session (1 hour):

    • Common problem patterns review
    • Debugging techniques demonstration
    • Q&A session

Assessment:

  • Students present their integrated ROS 2 system
  • Peer evaluation of system design and implementation

Week 3: Advanced ROS 2 Concepts and Integration​

Learning Objectives​

  • Students will integrate multiple ROS 2 concepts into a cohesive system
  • Students will implement error handling and system resilience
  • Students will optimize ROS 2 systems for performance

Day 1: System Integration Patterns​

Duration: 2 hours lecture + 1 hour lab prep

Activities:

  1. Lecture (60 min):

    • Design patterns for ROS 2 systems
    • Component architecture and reusability
    • Integration testing strategies

  2. Lab Preparation (30 min):

    • Prepare integrated project workspace
    • Review integration goals and milestones

Day 2-3: Project Implementation​

Duration: 2x 4-hour lab sessions

Activities:

  • Students work on comprehensive ROS 2 project
  • Instructors provide guidance and feedback
  • Daily checkpoints and progress reviews

Day 4: Performance Optimization​

Duration: 1 hour lecture + 2 hours lab

Activities:

  • Performance profiling techniques
  • Optimization strategies for real-time systems
  • Memory and computation efficiency

Day 5: Week Review and Assessment​

Duration: 3 hours project presentations

Week 4-5: Gazebo Simulation Environments​

Learning Objectives​

  • Students will create simulation environments in Gazebo
  • Students will implement robot models and sensors in simulation
  • Students will validate simulation against real-world data

Day 1: Gazebo Basics and Environment Creation​

Duration: 2 hours lecture + 1 hour lab

Activities:

  1. Lecture (60 min):

    • Gazebo architecture and physics engine
    • World file creation and customization
    • Sensor integration and configuration

  2. Hands-on Practice (60 min):

    • Create basic simulation environment
    • Add simple objects and terrain
    • Configure basic lighting and physics

Instructor Notes:

  • Prepare sample world files for students to modify
  • Check GPU compatibility for rendering-intensive simulations
  • Have simplified environments ready for slower systems

Day 2: Robot Models and Physics​

Duration: 1 hour lecture + 3 hours lab

Activities:

  1. Lecture (30 min):

    • URDF and SDF robot description formats
    • Physics properties and constraints
    • Collision and visual models

  2. Hands-on Practice (2.5 hours):

    • Model a simple robot in URDF
    • Import into Gazebo environment
    • Test physical interactions

Week 6: Sensors and Physics in Simulation​

Learning Objectives​

  • Students will integrate various sensor types in simulation
  • Students will implement realistic physics models
  • Students will validate simulation accuracy

Week 7-8: Perception and VSLAM with NVIDIA Isaac​

Learning Objectives​

  • Students will implement perception algorithms using Isaac tools
  • Students will create VSLAM systems for robot navigation
  • Students will integrate perception with control systems

Week 9: AI-Robot Brain Integration​

Learning Objectives​

  • Students will connect AI models to robot control systems
  • Students will implement decision-making algorithms
  • Students will optimize AI inference for robotic applications

Week 10-11: Vision-Language Integration​

Learning Objectives​

  • Students will connect vision models with language understanding
  • Students will implement multimodal fusion techniques
  • Students will create language-conditional perception systems

Week 12: Action Planning with LLMs​

Learning Objectives​

  • Students will integrate LLMs with robotic action planning
  • Students will implement grounded language understanding
  • Students will evaluate language-to-action systems

Week 13: Course Synthesis and Capstone Project​

Learning Objectives​

  • Students will integrate all course modules into a single system
  • Students will demonstrate comprehensive embodied AI system
  • Students will evaluate and present their final projects

Assessment Strategies​

Formative Assessment Tools​

  • Concept checks during lectures
  • Peer programming exercises
  • Real-time debugging challenges
  • Code review sessions

Summative Assessment Strategies​

  • Weekly lab reports with technical analysis
  • Programming assignments with rubric-based grading
  • Project presentations with demonstration
  • Comprehensive final project evaluation

Differentiation Strategies​

For Advanced Students​

  • Research paper implementations
  • Extension projects beyond required scope
  • Leadership roles in group activities
  • Mentorship of classmates

For Students Needing Additional Support​

  • Additional scaffolding and guided practice
  • Pair programming with stronger students
  • Extended lab time and additional office hours
  • Simplified project requirements with clear milestones

Common Student Misconceptions​

ROS 2-Specific Misconceptions​

  • Confusing asynchronous vs. synchronous communication
  • Misunderstanding node lifecycle and error handling
  • Confusing topics, services, and actions usage scenarios

Simulation-Specific Misconceptions​

  • Assuming simulation perfectly matches reality
  • Neglecting computational complexity in simulation
  • Overlooking physics parameter significance

AI Integration Misconceptions​

  • Expecting AI models to work without training/data
  • Underestimating computational requirements
  • Confusing perception with cognition

Accessibility Accommodations​

For Students with Different Learning Needs​

  • Multiple modalities for content delivery
  • Extended time for complex implementations
  • Alternative assessment methods when needed
  • Collaborative learning opportunities

Technical Troubleshooting​

Preemptive Measures​

  • System checks before each lab session
  • Backup hardware and software configurations
  • Step-by-step troubleshooting guides
  • Peer support systems

Common Technical Issues​

  • Network connectivity problems
  • Hardware calibration drift
  • Software dependency conflicts
  • Performance bottlenecks

Extension Activities​

For Extra Credit​

  • Advanced research paper implementations
  • Open source contribution projects
  • Cross-course integration projects
  • Conference-style presentations

Next Steps​

Continue to Assessment Rubrics or return to Instructor Guide Home.

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