  Learned visuomotor policies have shown considerable success as an alternative
to traditional, hand-crafted frameworks for robotic manipulation tasks.
Surprisingly, the extension of these methods to the multiview domain is
relatively unexplored. A successful multiview policy could be deployed on a
mobile manipulation platform, allowing it 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 from a
fixed perspective. We show that learning from multiview data has 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
with a degree of view-invariance.
