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Foundation of Physical AI

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Foundation of Physical AI engineering course illustration

Start with a public laboratory

16 short videos, 3 interactive laboratories and 3 readiness quizzes. Labs are open to everyone; sign in for free access to videos and course materials.

The course and certificate are free. Register, complete all lessons and pass all course quizzes to earn your certificate. Laboratory marks in the course are completion records, not an independent assessment of engineering competence.

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Physical AI Essentials book cover by Dr. Barak Or
Course Book

Physical AI Essentials

The complete book used throughout this course. Download the PDF for free. No sign-in or course registration required.

Download the Book PDF · 24 MB

About Course

The first course in the Physical AI learning path. It focuses on the foundations: the Physical AI stack, the physical interface, and learning from demonstrations. Public laboratories 1-3 are available to everyone; register for free to access all videos and course materials.

Recommendations
Perspectives from industry and research.
The course explains the principles of Physical AI clearly and brings them to life through hands-on simulator experience.
Alon WeinbergCisco Investments and M&A, Israel
The course teaches Physical AI principles clearly through simulator-based practice that connects engineering intuition with implementation.
Asaf FlishmanCEO FANUC Israel
An impressively comprehensive syllabus, spanning everything from kinematics and control to cutting-edge World Action Models. A great resource for anyone exploring Physical AI!
Yaniv HassidofPhD Candidate
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What Will You Learn?

  • Map the complete Physical AI stack from sensing to safe action
  • Specify state, observation, action, frame, timing, and control interfaces
  • Reason about kinematics, dynamics, contact, estimation, and partial observability
  • Design demonstration datasets and imitation-learning pipelines
  • Evaluate models in offline, closed-loop, and operational settings
  • Audit a Physical AI system before expensive training

Course Content

Lesson 1 · The Physical AI Stack
<p>Build an operational definition of Physical AI, distinguish the principal model families, place them inside a complete real-time stack, and evaluate the system at offline, closed-loop, and operational levels.</p>

  • Video 1.1 · What Makes AI Physical?
    04:10
  • Video 1.2 · The Closed Physical Feedback Loop
    03:45
  • Video 1.3 · VLMs, VLAs, and the Action Contract
    03:37
  • Video 1.4 · World Models, World Foundation Models, and WAMs
    03:52
  • Video 1.5 · JEPA, Robot Foundation Models, Simulators, and Digital Twins
    03:48
  • Video 1.6 · Hallucinations, Authorization, and System-Level Evaluation
    04:25
  • Lab 1 · Closed-Loop Robot Simulator
  • Lesson 1 · Physical AI Stack — Readiness Quiz

Lesson 1 · The Physical AI Stack
<p>Build an operational definition of Physical AI, distinguish the principal model families, place them inside a complete real-time stack, and evaluate the system at offline, closed-loop, and operational levels.</p>

Lesson 2 · The Physical Interface
<p>Specify the physical meaning of tensors and commands: state, observation, action, coordinate frames, camera geometry, mechanics, estimation, timing, and partial observability.</p>

Lesson 2 · The Physical Interface
<p>Specify the physical meaning of tensors and commands: state, observation, action, coordinate frames, camera geometry, mechanics, estimation, timing, and partial observability.</p>

Lesson 3 · Data and Learning from Demonstrations
<p>Treat demonstrations as sampled interaction trajectories, not exchangeable images. Design collection, imitation objectives, action chunks, normalization, splits, evaluation, and audits for real robot learning.</p>

Lesson 3 · Data and Learning from Demonstrations
<p>Treat demonstrations as sampled interaction trajectories, not exchangeable images. Design collection, imitation objectives, action chunks, normalization, splits, evaluation, and audits for real robot learning.</p>

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