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.
Specify the physical meaning of tensors and commands: state, observation, action, coordinate frames, camera geometry, mechanics, estimation, timing, and partial observability.
Treat demonstrations as sampled interaction trajectories, not exchangeable images. Design collection, imitation objectives, action chunks, normalization, splits, evaluation, and audits for real robot learning.
Interactive Laboratory
Build a warehouse robot policy through the Observe–Decide–Act–Verify loop, inspect uncertainty, and recover safely from failure.
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