  Reasoning about potential occlusions is essential for robots to efficiently
predict whether an object exists in an environment. Though existing work shows
that a robot with active perception can achieve various tasks, it is still
unclear if occlusion reasoning can be achieved. To answer this question, we
introduce the task of robotic object existence prediction: when being asked
about an object, a robot needs to move as few steps as possible around a table
with randomly placed objects to predict whether the queried object exists. To
address this problem, we propose a novel recurrent neural network model that
can be jointly trained with supervised and reinforcement learning methods using
a curriculum training strategy. Experimental results show that 1) both active
perception and occlusion reasoning are necessary to successfully achieve the
task; 2) the proposed model demonstrates a good occlusion reasoning ability by
achieving a similar prediction accuracy to an exhaustive exploration baseline
while requiring only about $10\%$ of the baseline's number of movement steps on
average; and 3) the model generalizes to novel object combinations with a
moderate loss of accuracy.
