  Imitation learning is a powerful paradigm for robot skill acquisition.
However, obtaining demonstrations suitable for learning a policy that maps from
raw pixels to actions can be challenging. In this paper we describe how
consumer-grade Virtual Reality headsets and hand tracking hardware can be used
to naturally teleoperate robots to perform complex tasks. We also describe how
imitation learning can learn deep neural network policies (mapping from pixels
to actions) that can acquire the demonstrated skills. Our experiments showcase
the effectiveness of our approach for learning visuomotor skills.
