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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.

Certification: $38 / ₪112, after all lessons are completed and all quizzes are passed. 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; videos and course materials require course access.

Industry recommendations
Hands-on Physical AI, validated by engineering leaders.
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
Course access is free for registered learners.Optional Physical AI Certification is available for $38 or ₪112, only after completing all course requirements.
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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>

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>

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