  Human-robot collaboration is on the rise. Robots need to increasingly improve
the efficiency and smoothness with which they assist humans by properly
anticipating a human's intention. To do so, prediction models need to increase
their accuracy and responsiveness. This work builds on top of Interaction
Movement Primitives with phase estimation and re-formulates the framework to
use dynamic human-motion observations which constantly update anticipatory
motions. The original framework only considers a single fixed-duration static
human observation which is used to perform only one anticipatory motion.
Dynamic observations, with built-in phase estimation, yield a series of updated
robot motion distributions. Co-activation is performed between the existing and
newest most probably robot motion distribution. This results in smooth
anticipatory robot motions that are highly accurate and with enhanced
responsiveness.
