Robot Learning

Explicit Policy

Explicit Policy is a policy represented as a direct feed-forward mapping from observations to actions, computed in a single network pass, in contrast to implicit policies that define actions indirectly as the minimizer of a learned energy function or the endpoint of an iterative sampling process. Explicit regression policies are fast and simple but represent only unimodal action distributions, averaging across conflicting demonstrations; the distinction was sharpened by the Implicit Behavioral Cloning work of 2021.

Why it matters for physical AI

The explicit-implicit trade-off, one fast forward pass versus expressive multimodal generation, drives current policy architecture choices and the push to distill diffusion policies into fast explicit ones for high-rate control.