Robot Learning

Normalizing Flow

A normalizing flow is a generative model that transforms a simple base distribution into a complex one through a sequence of invertible, differentiable mappings, allowing exact likelihood computation via the change-of-variables formula. Architectures such as RealNVP and neural spline flows are used in robotics for density estimation, multimodal policy distributions, and anomaly detection.

Why it matters for physical AI

Exact densities make flows useful for detecting out-of-distribution observations and for modeling multimodal action distributions where a Gaussian policy would average between valid behaviors.