  One of the most important challenges in robotics is producing accurate
trajectories and controlling their dynamic parameters so that the robots can
perform different tasks. The ability to provide such motion control is closely
related to how such movements are encoded. Advances on deep learning have had a
strong repercussion in the development of novel approaches for Dynamic Movement
Primitives. In this work, we survey scientific literature related to Neural
Dynamic Movement Primitives, to complement existing surveys on Dynamic Movement
Primitives.
