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Foundation of Physical AI

Categories: Physical AI
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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 and keep it open as you progress through the lessons.

Download the Book PDF · 24 MB

About Course

Open course · Based on Lesson 1 of Physical AI Essentials

Engineer intelligence that can act in the physical world.

A rigorous, practical introduction to the system stack, physical interfaces, and demonstration data required to build reliable embodied AI systems.

3Focused sessions
12Engineering lessons
OpenNo registration required

What you will be able to do

  • Map VLMs, VLAs, world models, WAMs, JEPAs, controllers, and safety monitors onto one Physical AI stack.
  • Specify state, observation, action, coordinate-frame, timing, and control interfaces without hidden ambiguity.
  • Reason about kinematics, dynamics, contact, state estimation, latency, and partial observability.
  • Design demonstration datasets, imitation-learning objectives, action chunks, splits, and closed-loop evaluations.
  • Audit a Physical AI pipeline before expensive model training begins.

Designed for

AI and robotics engineers, ML practitioners, technical product leaders, researchers, and advanced students who want a systems-level foundation for embodied intelligence.

Recommended background: basic machine learning, linear algebra, and software engineering. Robotics experience is helpful but not required.

Course sessions

Session 1 · The Physical AI StackFrom causal intervention and model classes to runtime authorization and operational evaluation.
Session 2 · The Physical InterfaceState, actions, frames, camera geometry, mechanics, estimation, time, and observability.
Session 3 · Data and Learning from DemonstrationsEpisode schemas, behavioral cloning, distribution shift, action chunks, data splits, and audits.
Based on Lesson 1 of the book: Physical AI Essentials by Dr. Barak Or. The course turns the book’s first lesson into concise, practical engineering sessions.
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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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