Robot Learning

Visuomotor Policy

A visuomotor policy is a control policy that maps raw camera observations, usually combined with proprioception, directly to motor commands, trained end to end rather than composed from separate perception, planning, and control modules. Levine et al.'s 2016 work on end-to-end training of deep visuomotor policies established the approach; contemporary instances include Diffusion Policy, ACT, and the action heads of vision-language-action models.

Why it matters for physical AI

End-to-end visuomotor learning removes the brittle hand-engineered interfaces between perception and control, and it is the core policy abstraction underneath essentially every current robot foundation model effort.