  Object finding in clutter is a skill that requires perception of the
environment and in many cases physical interaction. In robotics, interactive
perception defines a set of algorithms that leverage actions to improve the
perception of the environment, and vice versa use perception to guide the next
action. Scene interactions are difficult to model, therefore, most of the
current systems use predefined heuristics. This limits their ability to
efficiently search for the target object in a complex environment. In order to
remove heuristics and the need for explicit models of the interactions, in this
work we propose a reinforcement learning based active and interactive
perception system for scene exploration and object search. We evaluate our work
both in simulated and in real-world experiments using a robotic manipulator
equipped with an RGB and a depth camera, and compare our system to two
baselines. The results indicate that our approach, trained in simulation only,
transfers smoothly to reality and can solve the object finding task efficiently
and with more than 88% success rate.
