Skip to main content

Physical AI and Humanoid Robotics

Welcome to Physical AI and Humanoid Robotics: a comprehensive, hands-on course for building intelligent humanoid robots from the ground up.

Course Overview​

This course bridges the gap between artificial intelligence research and physical robotics implementation, providing you with the skills to:

  • Build and program humanoid robots using modern software stacks (ROS 2, NVIDIA Isaac Sim, PyTorch)
  • Integrate vision-language-action (VLA) models with real-time robot controllers
  • Design navigation, manipulation, and perception systems for real-world deployment
  • Deploy AI models on edge compute platforms (NVIDIA Jetson Orin)
  • Understand safety-critical robotics and human-robot interaction

Target Audience​

This course is designed for:

  • Software engineers transitioning to robotics
  • ML engineers wanting to deploy models on physical systems
  • Robotics students seeking industry-relevant skills
  • Researchers exploring embodied AI and humanoid robotics

Prerequisites​

  • Programming: Proficient in Python (NumPy, PyTorch basics); C++ knowledge is a plus
  • Math: Linear algebra (transforms, matrices), basic calculus, probability
  • Hardware: Access to NVIDIA RTX GPU (recommended: RTX 4070 Ti or higher) or NVIDIA Jetson Orin Nano/NX for edge deployment
  • Operating System: Ubuntu 22.04 LTS or WSL2 on Windows

Course Structure​

Module 1: The Robotic Nervous System​

Chapter 1: ROS 2 Fundamentals (Weeks 1-5)

  • Understanding robot middleware and communication paradigms
  • Building ROS 2 nodes, topics, services, and actions
  • Interfacing high-level AI with low-level controllers
  • Visualizing robot structures with URDF
  • Key Deliverable: A simulated robot arm that responds to basic vector commands

Module 2: The Digital Twin​

Chapter 2: Physics Simulation and Virtual Environments (Weeks 6-7)

  • Physics simulation in Gazebo (gravity, friction, collisions)
  • High-fidelity rendering in Unity for stakeholder demos and HRI testing
  • ROS 2 control integration (hardware-agnostic controllers)
  • Sensor simulation (LiDAR, depth cameras, IMU) with realistic noise models
  • Complete digital twin pipeline orchestration
  • Key Deliverable: A robot falling, balancing, and seeing its environment in a virtual world

Module 3: The AI-Robot Brain (Coming Soon)​

Scope: NVIDIA Isaac Sim & Isaac ROS, VSLAM, Nav2 (Weeks 8-10)

  • Visual Simultaneous Localization and Mapping (VSLAM)
  • Nav2 for autonomous path planning and navigation
  • GPU-accelerated perception with Isaac ROS
  • Key Deliverable: A robot that maps a room and navigates it autonomously

Module 4: Vision-Language-Action Models (Coming Soon)​

Scope: VLA Models, Whisper, LLM Integration (Weeks 11-13)

  • OpenAI Whisper for voice command processing
  • Large Language Models (LLMs) generating ROS 2 action plans
  • Vision-Language-Action model integration
  • Key Deliverable: Capstone Project - The Autonomous Humanoid (hears command, plans task, executes it)

Philosophy: Simulation First, Reality Second​

This course adheres to the "Digital Twin" philosophy:

  1. Develop in simulation (Gazebo, Unity, NVIDIA Isaac Sim) for rapid iteration and safety
  2. Validate hardware alignment (NVIDIA RTX GPUs, Jetson Orin)
  3. Deploy to physical systems with minimal sim-to-real gap

We prioritize modern stacks (ROS 2, lifecycle nodes, behavior trees) over legacy approaches, ensuring your skills remain industry-relevant.

Getting Started​

Ready to begin? Proceed to Chapter 1: The Robotic Nervous System (ROS 2) to understand how robots communicate and coordinate software components.

Development Environment Setup​

Before starting, ensure you have:

  • ROS 2 Humble or Iron installed on Ubuntu 22.04 LTS
  • Gazebo Classic or NVIDIA Isaac Sim for simulation
  • Python 3.10+ with rclpy, NumPy, and PyTorch

Detailed setup instructions are provided in each chapter's content.