  Object search -- the problem of finding a target object in a cluttered scene
-- is essential to solve for many robotics applications in warehouse and
household environments. However, cluttered environments entail that objects
often occlude one another, making it difficult to segment objects and infer
their shapes and properties. Instead of relying on the availability of CAD or
other explicit models of scene objects, we augment a manipulation planner for
cluttered environments with a state-of-the-art deep neural network for shape
completion as well as a volumetric memory system, allowing the robot to reason
about what may be contained in occluded areas. We test the system in a variety
of tabletop manipulation scenes composed of household items, highlighting its
applicability to realistic domains. Our results suggest that incorporating both
components into a manipulation planning framework significantly reduces the
number of actions needed to find a hidden object in dense clutter.
