  Learned visuomotor policies have shown considerable success as an alternative
to traditional, hand-crafted frameworks for robotic manipulation. Surprisingly,
an extension of these methods to the multiview domain is relatively unexplored.
A successful multiview policy could be deployed on a mobile manipulation
platform, allowing the robot to complete a task regardless of its view of the
scene. In this work, we demonstrate that a multiview policy can be found
through imitation learning by collecting data from a variety of viewpoints. We
illustrate the general applicability of the method by learning to complete
several challenging multi-stage and contact-rich tasks, from numerous
viewpoints, both in a simulated environment and on a real mobile manipulation
platform. Furthermore, we analyze our policies to determine the benefits of
learning from multiview data compared to learning with data collected from a
fixed perspective. We show that learning from multiview data results in little,
if any, penalty to performance for a fixed-view task compared to learning with
an equivalent amount of fixed-view data. Finally, we examine the visual
features learned by the multiview and fixed-view policies. Our results indicate
that multiview policies implicitly learn to identify spatially correlated
features.
