  We consider artificial agents that learn to jointly control their gripperand
camera in order to reinforcement learn manipulation policies in the presenceof
occlusions from distractor objects. Distractors often occlude the object of
in-terest and cause it to disappear from the field of view. We propose hand/eye
con-trollers that learn to move the camera to keep the object within the field
of viewand visible, in coordination to manipulating it to achieve the desired
goal, e.g.,pushing it to a target location. We incorporate structural biases of
object-centricattention within our actor-critic architectures, which our
experiments suggest tobe a key for good performance. Our results further
highlight the importance ofcurriculum with regards to environment difficulty.
The resulting active vision /manipulation policies outperform static camera
setups for a variety of clutteredenvironments.
