Robot Learning

Gaussian Mixture Model (GMM)

A Gaussian mixture model is a probabilistic model that represents a distribution as a weighted sum of Gaussian components, fitted with expectation-maximization. In robotics, GMMs have been widely used in learning from demonstration, where trajectories are encoded as mixtures and reproduced via Gaussian mixture regression, a line of work developed extensively by Calinon and colleagues. They also appear in clustering, multimodal state estimation, and as parametric action heads in some policies.

Why it matters for physical AI

Demonstrations of the same task vary, and GMMs offered an early principled way to capture that multimodality; the same need now drives diffusion and flow-based action heads in modern policies.