L3 — SYSTEMS~6 hours10 MODULES
Physical AI Foundations
What happens when large language models get a body. The full arc from GPT to VLA models to deployment in the real world.
PREREQUISITES:ML fundamentals helpful
VLAEmbodied AIFoundation ModelsPolicy Learning
CURATED BY
EAR Research Division
Based on papers from Physical Intelligence, Google DeepMind, Stanford, CMU
SALARY RANGE
$145K – $260K
For roles this path leads to
CAREER OUTCOMES
AI Robotics Researcher
Embodied AI Engineer
ML Engineer — Robotics
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WHAT YOU WILL LEARN
01Why embodiment changes AI fundamentally — not just an application
02The architecture of Vision-Language-Action (VLA) models
03How diffusion policy works for robotic manipulation
04Training data for robotics: Open-X Embodiment and DROID datasets
05π0, OpenVLA, RT-2, and Octo — the leading open models compared
06Simulation-to-real transfer: Isaac Lab, MuJoCo, and why the gap matters
07Evaluation methodologies: how do you actually measure robot capability?
08The research frontier: what's unsolved and where careers are forming
WHO IS THIS FOR
ML engineers, AI researchers, and technically literate people who want to understand why Physical AI is considered the most important frontier in the field — and how it actually works.
MODULES10 TOTAL · ~6 hours
#TITLETYPEDURATIONACCESS
01
Why physical AI is differentFREE
READING35 min02
The imitation learning paradigmFREE
READING40 min03
VLA architecture deep dive
READING50 min04
Diffusion policy
READING45 min05
The open model landscape: π0, OpenVLA, Octo
READING50 min06
Training data and the Open-X Embodiment dataset
READING40 min07
Simulation and sim-to-real
READING45 min08
Evaluation: how do you know if a robot works?
READING35 min09
The frontier: what's unsolved
READING40 min10
Where to go next in your career
READING30 minMORE AT THIS LEVELALL PATHS →
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