  We address the manipulation task of retrieving a target object from a
cluttered shelf. When the target object is hidden, the robot must search
through the clutter for retrieving it. Solving this task requires reasoning
over the likely locations of the target object. It also requires physics
reasoning over multi-object interactions and future occlusions. In this work,
we present a data-driven hybrid planner for generating occlusion-aware actions
in closed-loop. The hybrid planner explores likely locations of the occluded
target object as predicted by a learned distribution from the observation
stream. The search is guided by a heuristic trained with reinforcement learning
to act on observations with occlusions. We evaluate our approach in different
simulation and real-world settings (video available on
https://youtu.be/dY7YQ3LUVQg). The results validate that our approach can
search and retrieve a target object in near real time in the real world while
only being trained in simulation.
