  A Probabilistic Movement Primitive (ProMP) defines a distribution over
trajectories with an associated feedback policy. ProMPs are typically
initialized from human demonstrations and achieve task generalization through
probabilistic operations. However, there is currently no principled guidance in
the literature to determine how many demonstrations a teacher should provide
and what constitutes a "good" demonstration for promoting generalization. In
this paper, we present an active learning approach to learning a library of
ProMPs capable of task generalization over a given space. We utilize
uncertainty sampling techniques to generate a task instance for which a teacher
should provide a demonstration. The provided demonstration is incorporated into
an existing ProMP if possible, or a new ProMP is created from the demonstration
if it is determined that it is too dissimilar from existing demonstrations. We
provide a qualitative comparison between common active learning metrics;
motivated by this comparison we present a novel uncertainty sampling approach
named Greatest Mahalanobis Distance. We perform grasping experiments on a real
KUKA robot and show our novel active learning measure achieves better task
generalization with fewer demonstrations than a random sampling over the space.
