  To coordinate actions with an interaction partner requires a constant
exchange of sensorimotor signals. Humans acquire these skills in infancy and
early childhood mostly by imitation learning and active engagement with a
skilled partner. They require the ability to predict and adapt to one's partner
during an interaction. In this work we want to explore these ideas in a
human-robot interaction setting in which a robot is required to learn
interactive tasks from a combination of observational and kinesthetic learning.
To this end, we propose a deep learning framework consisting of a number of
components for (1) human and robot motion embedding, (2) motion prediction of
the human partner and (3) generation of robot joint trajectories matching the
human motion. To test these ideas, we collect human-human interaction data and
human-robot interaction data of four interactive tasks "hand-shake",
"hand-wave", "parachute fist-bump" and "rocket fist-bump". We demonstrate
experimentally the importance of predictive and adaptive components as well as
low-level abstractions to successfully learn to imitate human behavior in
interactive social tasks.
