  The vast majority of visual animals actively control their eyes, heads,
and/or bodies to direct their gaze toward different parts of their environment.
In contrast, recent applications of reinforcement learning in robotic
manipulation employ cameras as passive sensors. These are carefully placed to
view a scene from a fixed pose. Active perception allows animals to gather the
most relevant information about the world and focus their computational
resources where needed. It also enables them to view objects from different
distances and viewpoints, providing a rich visual experience from which to
learn abstract representations of the environment. Inspired by the primate
visual-motor system, we present a framework that leverages the benefits of
active perception to accomplish manipulation tasks. Our agent uses viewpoint
changes to localize objects, to learn state representations in a
self-supervised manner, and to perform goal-directed actions. We apply our
model to a simulated grasping task with a 6-DoF action space. Compared to its
passive, fixed-camera counterpart, the active model achieves 8% better
performance in targeted grasping. Compared to vanilla deep Q-learning
algorithms, our model is at least four times more sample-efficient,
highlighting the benefits of both active perception and representation
learning.
