LEARNING PATH
EMBODIED AI
From perception to action: learn embodied AI.
Introduction to Physical AI: Connecting the history of VLA from perception to safe behavior
Instead of viewing Vision-Language-Action as the magic of a universal robot, understand it as a connection between observation, coordinates, strategy, and control.
2025 VLA map: read SmolVLA, π0/π0.5, OpenVLA, and GR00T using the same yardstick
Updated notes that interpret each presentation in terms of openness, body, data, behavioral expression, and evaluation boundaries, rather than ranking each presentation in a comparison table.
LeRobot practice: Make dataset, imitation learning, evaluation, and Sim2Real into one quality loop
Starting with LeRobot's data format and unified interface, learn the points of contact that are likely to fail from collection to actual machine evaluation.
Starting Physical AI without a robot: Safe learning procedure and decision not to proceed to the actual machine
It clearly states that the hardware is not running, and touches on the core of Physical AI just through data observation, simulation, and evaluation design.
Physical AI coordinate transformation and camera calibration: do not treat image pixels as safe robot commands
Learn camera, robot base, and hand coordinate systems and calibration as verifiable contracts outside of VLA.
Reading VLA papers to implementable designs: Comparative reading exercise of π0, SmolVLA, and GR00T
The novelty of a paper is not confused with performance ranking, but is broken down into behavioral expression, data, body, evaluation, and unverified boundaries.
Physical AI workshop: from data splits to policy evaluation and Sim2Real decisions
Turn public or synthetic episodes into reproducible splits, baseline and adaptation evaluations, failure analysis, and a justified decision not to proceed to hardware.