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Physical AI & Humanoid Robotics - Book Structure

Course Overview​

Duration: 16 Weeks Format: Self-paced with weekly milestones Prerequisites: Basic Python programming, Linear algebra fundamentals Learning Outcomes: Build and deploy intelligent physical AI systems using ROS 2, simulation tools, and vision-language-action models

Module Structure (4 Modules × 4 Weeks Each)​

📘 Module 1: Foundations of Physical AI (Weeks 1-4)​

Goal: Master robotics fundamentals and ROS 2 basics

Week 1: Introduction to Physical AI​

  • Chapter 1: What is Physical AI?
    • Physical AI vs Traditional Robotics
    • The Embodied AI Revolution
    • Key Components: Perception, Planning, Action
    • Real-world Applications (Manufacturing, Healthcare, Home)
  • Lab: Setup development environment (ROS 2 Humble, Docker)
  • Assignment: Write a physical AI system requirements document

Week 2: ROS 2 Fundamentals​

  • Chapter 2: Robot Operating System 2
    • ROS 2 Architecture (Nodes, Topics, Services, Actions)
    • Publisher/Subscriber Pattern
    • Message Types and Custom Interfaces
    • Launch Files and Parameters
  • Lab: Build a publisher/subscriber system
  • Code Examples: 5 working ROS 2 nodes
  • Assignment: Create a multi-node robot control system

Week 3: Robot Kinematics​

  • Chapter 3: Forward and Inverse Kinematics
    • Denavit-Hartenberg Parameters
    • Homogeneous Transformations
    • Forward Kinematics (Position & Orientation)
    • Inverse Kinematics (Analytical & Numerical)
    • Jacobians and Velocity Kinematics
  • Lab: Implement FK/IK for 6-DOF manipulator
  • Math: 15+ equations with worked examples
  • Assignment: Solve IK for humanoid arm reaching task

Week 4: Robot Dynamics and Control​

  • Chapter 4: Dynamics and Control Systems
    • Lagrangian Mechanics for Robots
    • Joint Torque Calculation
    • PID Control
    • Trajectory Planning (Joint Space vs Task Space)
    • Impedance Control for Physical Interaction
  • Lab: Tune PID controller for robotic arm
  • Simulation: Gazebo dynamic simulation
  • Assignment: Design trajectory planner for pick-and-place

🎮 Module 2: Simulation and Perception (Weeks 5-8)​

Goal: Master simulation environments and sensor processing

Week 5: Gazebo and Unity Simulation​

  • Chapter 5: Simulation Environments
    • Gazebo Classic vs Ignition Fortress
    • Unity Robotics Hub
    • URDF/USD Model Creation
    • Physics Engines (ODE, PhysX, Unity Physics)
    • ROS 2 Bridge Integration
  • Lab: Spawn humanoid robot in Gazebo and Unity
  • Code Examples: 3 simulation environments
  • Assignment: Create custom robot URDF with sensors

Week 6: NVIDIA Isaac Sim​

  • Chapter 6: GPU-Accelerated Simulation
    • Isaac Sim Architecture
    • Synthetic Data Generation (Replicator)
    • Domain Randomization
    • Isaac ROS Integration
    • Performance Optimization
  • Lab: Generate 1,000 annotated training images
  • Assignment: Build synthetic dataset for object detection

Week 7: Computer Vision for Robotics​

  • Chapter 7: Perception Systems
    • Camera Calibration
    • 2D Object Detection (YOLO, Detectron2)
    • 3D Pose Estimation (PnP, FoundationPose)
    • Point Cloud Processing (PCL)
    • Depth Estimation (Stereo, Monocular)
  • Lab: Integrate camera into ROS 2 pipeline
  • Code Examples: Real-time object detection node
  • Assignment: Build grasp pose estimator

Week 8: SLAM and Navigation​

  • Chapter 8: Spatial Awareness
    • SLAM Algorithms (Cartographer, RTAB-Map)
    • Localization (AMCL)
    • Path Planning (A*, RRT, Nav2)
    • Obstacle Avoidance
    • Sensor Fusion (LiDAR + Camera)
  • Lab: Build autonomous navigation system
  • Simulation: Navigate humanoid robot in complex environment
  • Assignment: Implement multi-floor navigation

🤖 Module 3: Humanoid Robotics (Weeks 9-12)​

Goal: Master bipedal locomotion and manipulation

Week 9: Bipedal Locomotion​

  • Chapter 9: Walking and Balance
    • Zero Moment Point (ZMP)
    • Center of Mass (CoM) Control
    • Footstep Planning
    • Balance Controllers
    • Gait Generation
  • Lab: Implement ZMP walking controller
  • Simulation: Make humanoid walk in Isaac Sim
  • Assignment: Design stair-climbing gait

Week 10: Manipulation and Grasping​

  • Chapter 10: Robotic Manipulation
    • Grasp Planning (Parallel Jaw, Multi-fingered)
    • Motion Planning (MoveIt 2)
    • Collision Avoidance
    • Force Control
    • Dual-Arm Coordination
  • Lab: Plan and execute pick-and-place
  • Code Examples: MoveIt 2 integration
  • Assignment: Build dual-arm manipulation system

Week 11: Whole-Body Control​

  • Chapter 11: Integrated Control Systems
    • Hierarchical Control
    • Task Priority (Locomotion + Manipulation)
    • Contact Force Optimization
    • Dynamic Balance During Manipulation
    • Fall Recovery
  • Lab: Humanoid picks object while walking
  • Simulation: Full-body reaching task
  • Assignment: Design whole-body controller for door opening

Week 12: Physical Human-Robot Interaction​

  • Chapter 12: Safe Interaction
    • Collision Detection and Reaction
    • Compliant Control
    • Safety Standards (ISO 13482)
    • Gesture Recognition
    • Social Navigation
  • Lab: Implement safety controller
  • Assignment: Design HRI scenario with safety analysis

🧠 Module 4: AI Integration and Deployment (Weeks 13-16)​

Goal: Integrate LLMs/VLAs and deploy complete systems

Week 13: Vision-Language-Action Models​

  • Chapter 13: VLA Architectures
    • RT-1 and RT-2 Models
    • Octo: Open-Source VLA
    • OpenVLA Architecture
    • Training Data Collection
    • Fine-tuning for Custom Tasks
  • Lab: Deploy OpenVLA on robot
  • Code Examples: VLA inference pipeline
  • Assignment: Fine-tune VLA for household tasks

Week 14: LLM-Based Task Planning​

  • Chapter 14: Language-Driven Control
    • LLM as Task Planner (GPT-4, Claude)
    • Prompt Engineering for Robotics
    • Grounding Language to Actions
    • Error Recovery with LLMs
    • Multimodal Reasoning (PaLM-E)
  • Lab: Build voice-controlled robot
  • Code Examples: Whisper + LLM + ROS 2 pipeline
  • Assignment: Create natural language interface for manipulation

Week 15: System Integration​

  • Chapter 15: End-to-End Systems
    • Multi-Agent Coordination
    • Perception-Planning-Action Loop
    • Real-Time Performance Optimization
    • Logging and Debugging
    • Testing and Validation
  • Lab: Integrate all components
  • Assignment: Build complete physical AI system

Week 16: Deployment and Final Project​

  • Chapter 16: Production Deployment
    • Containerization (Docker)
    • Edge Deployment (Jetson, Raspberry Pi)
    • Cloud Integration
    • Monitoring and Maintenance
    • Case Studies
  • Final Project: Deploy autonomous humanoid system
    • Scenario: Home assistant robot (navigation + manipulation + voice)
    • Deliverables: Code, documentation, demo video

Weekly Syllabus Summary​

WeekModuleChapterFocusDeliverable
11Ch 1Physical AI ConceptsEnvironment Setup + Requirements Doc
21Ch 2ROS 2 FundamentalsMulti-node Control System
31Ch 3Robot KinematicsFK/IK Implementation
41Ch 4Dynamics & ControlTrajectory Planner
52Ch 5Gazebo/Unity SimulationCustom Robot URDF
62Ch 6Isaac SimSynthetic Dataset (1k images)
72Ch 7Computer VisionGrasp Pose Estimator
82Ch 8SLAM & NavigationAutonomous Navigation System
93Ch 9Bipedal LocomotionZMP Walking Controller
103Ch 10ManipulationPick-and-Place System
113Ch 11Whole-Body ControlWalking + Manipulation
123Ch 12Human-Robot InteractionSafety Controller
134Ch 13VLA ModelsVLA Deployment
144Ch 14LLM Task PlanningVoice-Controlled Robot
154Ch 15System IntegrationComplete Physical AI System
164Ch 16DeploymentFinal Project: Home Assistant Robot

Learning Progression​

Beginner (Weeks 1-4)​

  • Install and configure ROS 2
  • Write basic publisher/subscriber nodes
  • Understand robot coordinate frames
  • Implement simple controllers

Intermediate (Weeks 5-8)​

  • Build simulation environments
  • Process camera and LiDAR data
  • Implement object detection
  • Create autonomous navigation

Advanced (Weeks 9-12)​

  • Program bipedal walking
  • Plan manipulation tasks
  • Coordinate whole-body motion
  • Design safe human-robot interaction

Expert (Weeks 13-16)​

  • Integrate VLA models
  • Build language-driven systems
  • Deploy to real hardware
  • Complete capstone project

Assessment Structure​

Weekly (16 assignments × 5 points each = 80 points)​

  • Code submissions with documentation
  • Simulation demonstrations
  • Mathematical derivations

Final Project (20 points)​

  • System design document (5 pts)
  • Implementation (10 pts)
  • Demo video (3 pts)
  • Code quality (2 pts)

Total: 100 points

Hardware Requirements​

Minimum​

  • CPU: Intel i5 or AMD Ryzen 5
  • RAM: 16GB
  • Storage: 50GB SSD
  • OS: Ubuntu 22.04 (or Docker on Windows/Mac)
  • GPU: NVIDIA RTX 3060+ (6GB VRAM)
  • CPU: Intel i7 or AMD Ryzen 7
  • RAM: 32GB
  • Storage: 100GB NVMe SSD

Optional (Physical Hardware)​

  • Raspberry Pi 4 (4GB) or NVIDIA Jetson Nano
  • USB Camera (1080p)
  • Arduino/ESP32 for motor control
  • Small robotic arm (optional)

Software Stack​

Core Tools​

  • ROS 2 Humble Hawksbill
  • Python 3.10+
  • C++ (optional for performance-critical nodes)

Simulation​

  • Gazebo Classic 11 / Ignition Fortress
  • Unity 2022 LTS + Robotics Hub
  • NVIDIA Isaac Sim 2023.1+

AI/ML​

  • PyTorch 2.0+
  • OpenVLA (Hugging Face)
  • OpenAI API / Anthropic Claude API
  • Whisper (speech recognition)

Development​

  • VS Code with ROS extensions
  • Docker Desktop
  • Git version control

Support Resources​

Documentation​

Community​

  • ROS Discourse: https://discourse.ros.org/
  • GitHub Issues: Report bugs and request features
  • Office Hours: Weekly Q&A sessions (TBD)

Next: Proceed to Chapter 1: Introduction to Physical AI