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Instructor Guide - Introduction

Welcome to the instructor guide for the Physical AI & Humanoid Robotics course. This guide provides comprehensive resources to help you effectively teach the course, including weekly lesson plans, assessment rubrics, pedagogical notes, and troubleshooting guides.

Course Overview​

This 13-week course introduces students to the integration of artificial intelligence with robotics, specifically focusing on embodied intelligence. The course is structured around four core modules:

  1. ROS 2 (Weeks 1-3): Robot Operating System as the nervous system of robotics
  2. Gazebo/Unity (Weeks 4-6): Simulation environments as digital twins
  3. NVIDIA Isaac (Weeks 7-9): AI-robot brains with perception and VSLAM
  4. Vision-Language-Action (VLA) (Weeks 10-13): Integrating vision, language, and action planning

Learning Objectives Alignment​

Each module and week has specific learning objectives aligned with the overall course goals:

  • Students will understand the architecture and implementation of modern robotic systems
  • Students will connect AI models to physical robotic platforms
  • Students will develop embodied AI systems that can interpret natural language commands
  • Students will evaluate and validate robotic system performance

Required Resources​

Hardware Requirements​

  • NVIDIA RTX workstation with GPU for AI model training and inference
  • Intel RealSense cameras for perception
  • NVIDIA Jetson platforms for robot deployment
  • Robot platforms with 6+ DOF manipulators (UR3, Panda, or equivalent)
  • Additional sensors (LiDAR, force/torque sensors)

Software Requirements​

  • Ubuntu 22.04 LTS
  • ROS 2 Humble Hawksbill
  • NVIDIA Isaac ROS packages
  • Gazebo Harmonic or Unity Robotics
  • Python 3.10+ with relevant AI libraries
  • Docker for containerized deployment

Assessment Strategy​

Formative Assessment​

  • Weekly lab exercises with peer review
  • In-class coding challenges
  • Concept checks during lectures
  • Discussion forums for troubleshooting

Summative Assessment​

  • Weekly quizzes (multiple choice and short answer)
  • Programming assignments with rubric-based grading
  • Lab reports with technical analysis
  • Final capstone project with presentation

Pedagogical Approach​

The course employs a hands-on, project-based learning approach that emphasizes:

  1. Embodied Cognition: Learning through doing, with physical or simulated robots
  2. Spiral Curriculum: Revisiting concepts with increasing complexity
  3. Collaborative Learning: Group projects and peer programming
  4. Real-World Application: Connecting theoretical concepts to practical implementations

Accessibility Considerations​

For Students with Disabilities​

  • Provide alternative text for all images and diagrams
  • Offer multiple modalities for content consumption (text, video, interactive)
  • Ensure compatibility with assistive technologies
  • Provide extended time for practical assessments

For Students with Different Technical Backgrounds​

  • Offer pre-course preparation materials for students with limited programming experience
  • Provide additional resources for students with stronger backgrounds to explore advanced topics
  • Create peer-mentoring opportunities to support collaborative learning

Course Schedule Recommendations​

The course can be taught in various formats:

13-Week Semester Format​

  • 2-3 hours of lectures per week
  • 3-4 hours of lab time per week
  • Weekly assignments due before the next week's lab
  • Midterm and final projects

Intensive Workshop Format​

  • 6-8 hours per day for 13 days
  • Alternating lecture and hands-on sessions
  • Daily assignments and checkpoints
  • Final project due at the end of the workshop

Getting Started​

This instructor guide contains detailed information for each week, including:

  • Learning Objectives: What students should know and be able to do
  • Lesson Plans: Detailed daily schedules with activities
  • Assessment Rubrics: Grading criteria for assignments and projects
  • Troubleshooting Guides: Common issues and solutions
  • Extension Activities: For students needing additional challenges
  • Resource Lists: Additional materials for deeper exploration

Use this guide in conjunction with the student-facing materials to ensure alignment between instruction and student expectations. Each week builds upon the previous, so maintaining the sequence is important for student success.

Technical Preparation​

Before beginning the course, ensure that all technical infrastructure is in place:

  1. Robot Platforms: Test all robot platforms with required software packages
  2. Simulation Environment: Verify that all students can access and run simulations
  3. Software Installation: Prepare installation scripts and images to streamline setup
  4. Network Configuration: Ensure stable network connections for collaborative tools and software repositories
  5. Safety Protocols: Review and implement all safety measures for robot operation

Support Resources​

For technical support and community discussion, join our instructor community:

  • Documentation: [Course Documentation Website]
  • Support Forum: [Link to Discussion Forum]
  • Video Tutorials: [Link to Tutorial Repository]
  • Office Hours: Regular office hours with course developers

Next Steps​

Continue to the Hardware Setup Guide for detailed instructions on preparing your teaching environment and student lab stations.

Next: Hardware Setup for Instructors → | Week 1 Lesson Plan →