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Introduction to Physical AI & Humanoid Robotics

Learning Objectives

  • Understand the concept of Physical AI and why it's essential for modern robotics
  • Identify the core components of Physical AI systems (perception, planning, action, learning)
  • Explore the structure of this Hackathon Edition course and its module flow
  • Select the correct hardware or simulation path for completing the hackathon tasks

Welcome to the Physical AI & Humanoid Robotics — Hackathon Edition.
This version of the course focuses on practical, hands-on robotics skills you’ll need for building a working humanoid system within a limited timeframe.

What is Physical AI?​

Physical AI refers to artificial intelligence that goes beyond the screen and interacts with the real world. Unlike traditional AI models that operate only on data, Physical AI connects sensing, reasoning, and robot actions through:

  • Perception – Cameras, LiDAR, depth sensors, voice input
  • Planning – Deciding what to do next in a dynamic world
  • Action – Motors, actuators, and manipulation tasks
  • Learning – Improving performance from experience or data

These four components form the backbone of your upcoming hackathon tasks.

Course Overview​

This Hackathon Edition compresses the original 13-week curriculum into a rapid, targeted learning journey, preparing you for real-world humanoid robotics challenges.

Weeks 1–2: Foundations​

  • Overview of Physical AI and robotics pipeline
  • Hardware setup (Workstation, Edge Kit, or Cloud Simulation)
  • Environment configuration and productivity tools

Module 1: The Robotic Nervous System (Weeks 3–5)​

Master ROS 2, the communication backbone of modern robots.
You'll build nodes, topics, services, and URDF models used in humanoids.

Module 2: Digital Twins (Weeks 6–7)​

Use Gazebo and Unity to develop digital replicas of your robot.
Simulations help you test hackathon tasks faster and safely.

Module 3: NVIDIA Isaac (Weeks 8–10)​

Implement perception and navigation pipelines using Isaac Sim, including:

  • VSLAM
  • 3D perception
  • Basic navigation

Module 4: VLA & Humanoids (Weeks 11–13)​

Integrate Vision-Language-Action models to control humanoid robots.
You’ll work with:

  • Kinematics
  • Manipulation tasks
  • Voice/LLM-based control

Week 13: Capstone / Hackathon Build​

Construct a simplified humanoid workflow:
Voice Input → Plan → Navigate → Perceive → Manipulate

Who This Course Is For​

This edition is ideal for:

  • Hackathon participants
  • Developers wanting rapid robotics exposure
  • Engineers who need hands-on robotics project experience
  • Students preparing for robotics competitions

Prerequisites​

  • Python experience
  • Ubuntu/Linux basics
  • Linear algebra fundamentals
  • 8–12 hours/week for practice

Learning Approach​

Every chapter includes:

  1. Learning Objectives
  2. Prerequisites
  3. Main Content
  4. Hands-On Exercises
  5. Summary
  6. References & Resources

Getting Started​

  1. Choose your hardware entry point:

  2. Review essential robotics terms in the Glossary

  3. Begin Module 1: ROS 2 Architecture

Assessment Structure​

You will complete:

  • ROS 2 Mini Project
  • Simulation Environment Build
  • Isaac Perception Pipeline
  • Hackathon Capstone

Each includes rubrics for: Needs Improvement, Proficient, Excellent.

Support and Resources​

  • Robotics Glossary
  • Hardware Setup Guides
  • Module-by-module troubleshooting notes

Ready to start? Pick your setup path and begin building your Physical AI skills!