  Rearrangement planning for object retrieval tasks from confined spaces is a
challenging problem, primarily due to the lack of open space for robot motion
and limited perception. Several traditional methods exist to solve object
retrieval tasks, but they require overhead cameras for perception and a
time-consuming exhaustive search to find a solution and often make unrealistic
assumptions, such as having identical, simple geometry objects in the
environment. This paper presents a neural object retrieval framework that
efficiently performs rearrangement planning of unknown, arbitrary objects in
confined spaces to retrieve the desired object using a given robot grasp. Our
method actively senses the environment with the robot's in-hand camera. It then
selects and relocates the non-target objects such that they do not block the
robot path homotopy to the target object, thus also aiding an underlying path
planner in quickly finding robot motion sequences. Furthermore, we demonstrate
our framework in challenging scenarios, including real-world cabinet-like
environments with arbitrary household objects. The results show that our
framework achieves the best performance among all presented methods and is, on
average, two orders of magnitude computationally faster than the
best-performing baselines.
