  Humans intuitively solve tasks in versatile ways, varying their behavior in
terms of trajectory-based planning and for individual steps. Thus, they can
easily generalize and adapt to new and changing environments. Current Imitation
Learning algorithms often only consider unimodal expert demonstrations and act
in a state-action-based setting, making it difficult for them to imitate human
behavior in case of versatile demonstrations. Instead, we combine a mixture of
movement primitives with a distribution matching objective to learn versatile
behaviors that match the expert's behavior and versatility. To facilitate
generalization to novel task configurations, we do not directly match the
agent's and expert's trajectory distributions but rather work with concise
geometric descriptors which generalize well to unseen task configurations. We
empirically validate our method on various robot tasks using versatile human
demonstrations and compare to imitation learning algorithms in a state-action
setting as well as a trajectory-based setting. We find that the geometric
descriptors greatly help in generalizing to new task configurations and that
combining them with our distribution-matching objective is crucial for
representing and reproducing versatile behavior.
