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Curriculum Mapping: 13-Week Physical AI & Humanoid Robotics Course

🟡Intermediate

This document provides detailed mapping between the textbook content and the official 13-week Physical AI & Humanoid Robotics curriculum. Each week's learning objectives, topics, and expected outcomes are aligned with specific textbook sections.

Course Overview​

Course Title: Physical AI & Humanoid Robotics Duration: 13 weeks Target Audience: Panaversity/GIAIC/PIAIC students Prerequisites: Basic programming knowledge, linear algebra, calculus

Weekly Breakdown​

Week 1: ROS 2 Fundamentals​

Textbook Chapters: Chapter 1 (ROS 2 Basics) Learning Objectives:

  • Understand ROS 2 architecture and core concepts
  • Create and run basic publisher/subscriber nodes
  • Use ROS 2 tools for debugging and monitoring

Topics Covered:

  • ROS 2 nodes, topics, and services
  • Message passing and communication patterns
  • Basic ROS 2 tools (ros2 topic, ros2 node, etc.)

Expected Outcomes:

  • Students can create simple ROS 2 publisher and subscriber nodes
  • Students can monitor ROS 2 communication using command-line tools
  • Students understand the publish/subscribe pattern

Lab Activities:

  • Lab 1: Your First ROS 2 Publisher and Subscriber
  • Practice with ROS 2 command-line tools

Week 2: Isaac Sim and Digital Twins​

Textbook Chapters: Chapter 2 (Isaac Sim Setup) Learning Objectives:

  • Set up and configure Isaac Sim environment
  • Create basic robot models in simulation
  • Understand digital twin concepts

Topics Covered:

  • Isaac Sim installation and configuration
  • URDF models and robot description
  • Simulation environments and physics

Expected Outcomes:

  • Students can launch basic simulations in Isaac Sim
  • Students can load and manipulate robot models
  • Students understand the role of simulation in robotics

Lab Activities:

  • Lab 2: Basic Robot Simulation in Isaac Sim
  • Creating and testing simple robot models

Week 3: Robot Control Theory​

Textbook Chapters: Chapter 3 (Robot Control Theory) Learning Objectives:

  • Understand fundamental control concepts for robots
  • Implement basic movement controllers
  • Connect ROS 2 with simulation environments

Topics Covered:

  • PID controllers and feedback systems
  • Joint space vs. Cartesian space control
  • Trajectory planning basics

Expected Outcomes:

  • Students can implement simple joint controllers
  • Students understand feedback control principles
  • Students can command basic robot movements

Lab Activities:

  • Lab 3: Joint Control Implementation
  • Basic movement and trajectory following

Week 4: Perception Systems​

Textbook Chapters: Chapter 4 (Perception Systems) Learning Objectives:

  • Understand robot perception fundamentals
  • Process sensor data using ROS 2
  • Integrate cameras and other sensors

Topics Covered:

  • Sensor types and characteristics
  • Image processing basics
  • Sensor fusion concepts

Expected Outcomes:

  • Students can process camera data in ROS 2
  • Students understand basic computer vision for robotics
  • Students can integrate multiple sensors

Lab Activities:

  • Lab 4: Camera Data Processing
  • Multi-sensor integration exercises

Week 5: Vision-Language-Action Models​

Textbook Chapters: Chapter 5 (Vision-Language-Action) Learning Objectives:

  • Understand VLA (Vision-Language-Action) model concepts
  • Integrate LLMs with robot control
  • Connect perception to action

Topics Covered:

  • Vision-language models in robotics
  • Action planning from natural language
  • Integration of perception and action systems

Expected Outcomes:

  • Students can implement basic VLA pipelines
  • Students understand how to translate language to actions
  • Students can connect perception to control systems

Lab Activities:

  • Lab 5: Basic VLA Implementation
  • Language-to-action mapping exercises

Week 6: Humanoid Locomotion​

Textbook Chapters: Chapter 6 (Humanoid Locomotion) Learning Objectives:

  • Understand humanoid robot kinematics
  • Implement walking and balance algorithms
  • Control complex multi-DOF systems

Topics Covered:

  • Forward and inverse kinematics
  • Walking pattern generation
  • Balance and stability control

Expected Outcomes:

  • Students can implement basic walking patterns
  • Students understand kinematic chains
  • Students can control humanoid balance

Lab Activities:

  • Lab 6: Basic Walking Pattern Implementation
  • Balance and stability exercises

Week 7-13: Capstone Project​

Textbook Chapters: Chapter 7 (Capstone Project) Learning Objectives:

  • Integrate all learned concepts into a complete system
  • Implement voice → LLM → ROS 2 action flow
  • Create autonomous humanoid robot behaviors

Topics Covered:

  • System integration and architecture
  • Autonomous behavior implementation
  • Real-time performance optimization

Expected Outcomes:

  • Students can build complete autonomous systems
  • Students understand system-level integration challenges
  • Students can implement voice-commanded robot behaviors

Lab Activities:

  • Lab 7: Capstone Project Implementation
  • Final demonstration and evaluation

Module Alignment​

The curriculum is organized into 4 core modules that align with the textbook's structure:

Module 1: ROS 2 (Robotic Nervous System)​

  • Weeks 1-2: Communication and simulation foundations
  • Focus on ROS 2 architecture and simulation environments

Module 2: Gazebo + Unity (Digital Twin)​

  • Weeks 2-3: Simulation and digital twin concepts
  • Focus on creating and manipulating virtual environments

Module 3: NVIDIA Isaac (AI-Robot Brain)​

  • Weeks 4-6: Perception and control systems
  • Focus on AI-driven robot behavior and control

Module 4: VLA Robotics (Vision-Language-Action)​

  • Weeks 5-7: Integration of perception, language, and action
  • Focus on complete autonomous systems

Assessment Methods​

Formative Assessment​

  • Weekly lab exercises with immediate feedback
  • Peer review of code implementations
  • Continuous monitoring of progress

Summative Assessment​

  • Weekly quizzes on theoretical concepts
  • Lab practicals demonstrating implementation skills
  • Capstone project demonstration and documentation

Learning Outcomes Alignment​

Upon completion of this course, students will be able to:

  1. Technical Skills:

    • Implement ROS 2-based robotic systems
    • Create and simulate humanoid robots in Isaac Sim
    • Integrate perception, planning, and control systems
    • Develop autonomous behaviors using VLA models

  2. Problem-Solving:

    • Analyze complex robotics problems
    • Design appropriate system architectures
    • Troubleshoot and debug robotic systems

  3. Integration:

    • Combine multiple technologies into cohesive systems
    • Understand trade-offs in system design
    • Optimize system performance

Prerequisites Mapping​

Each week builds upon previous knowledge:

  • Weeks 1-3: Programming fundamentals, basic ROS 2 knowledge
  • Weeks 4-5: Understanding of control systems
  • Weeks 6-7: Knowledge of kinematics and dynamics
  • Weeks 8-13: Integration of all previous concepts

Technology Stack Alignment​

All content aligns with the target technology stack:

  • Ubuntu 22.04: All examples and labs tested on this OS
  • ROS 2 Kilted Kaiju: All ROS 2 examples use this version
  • NVIDIA Isaac Sim 2025: Simulation examples use this version
  • Python 3.10+: All code examples use this Python version

Hardware Alignment​

Content is designed to work with the recommended hardware:

  • Jetson Orin Nano Super: Edge computing for humanoid robots
  • Unitree G1: Physical platform for real-world testing
  • Intel RealSense D435i: Depth sensing and perception

This curriculum mapping ensures 100% alignment between the textbook content and the official 13-week breakdown with 4 modules as specified in the course requirements.