Robot Learning

Sensorimotor Learning

Sensorimotor learning is the acquisition of coupled perception-action skills, where a robot learns to map raw sensory streams directly to motor commands rather than passing through hand-designed intermediate representations. End-to-end visuomotor policy training (Levine et al., 2016) demonstrated that convolutional networks could learn such mappings on physical manipulators. The term borrows from neuroscience, where it describes how animals refine movement through sensory feedback.

Why it matters for physical AI

Tight perception-action coupling captures contact-rich subtleties that modular pipelines discard, and it is the operating principle behind modern visuomotor policies and vision-language-action models.