Robot Learning

State Abstraction

State abstraction is the mapping of raw, high-dimensional states or observations into compact representations that preserve the information needed for decision making while discarding irrelevant detail. Formal treatments include bisimulation metrics, which group states with equivalent reward and transition behavior, while practical robot learning realizes abstraction through learned latent states, object-centric representations, or hand-chosen features. Good abstractions shrink the effective problem and improve generalization.

Why it matters for physical AI

Cameras deliver megapixels while tasks depend on a handful of object poses; how a policy compresses that gap largely determines its sample efficiency and transfer across scenes.