  Probabilistic Movement Primitives (ProMPs) are a widely used representation
of movements for human-robot interaction. They also facilitate the
factorization of temporal and spatial structure of movements. In this work we
investigate a method to temporally align observations so that when learning
ProMPs, information in the spatial structure of the observed motion is
maximized while maintaining a smooth phase velocity. We apply the method on
recordings of hand trajectories in a two-dimensional reaching task. A system
for simultaneous recognition of movement and phase is proposed and performance
of movement recognition and movement reproduction is discussed.
