When we practice a movement, human brains creates a
motor memory of it. These memories are formed and stored
in the brain as representations which allows us to perform

familiar tasks faster than new movements. From a devel-
opmental robotics and embodied artificial agent perspective

it could be also beneficial to exploit the concept of these
motor representations in the form of spatial-temporal motion
priors for complex, full-body motion synthesis. Encoding such
priors in neural networks in a form of inductive biases inherit
essential spatio-temporality aspect of human motion. In our
current work we examine and compare recent approaches for
capturing spatial and temporal dependencies with machine
learning algorithms that are used to model human motion.