  Efficiently finding an occluded object with lateral access arises in many
contexts such as warehouses, retail, healthcare, shipping, and homes. We
introduce LAX-RAY (Lateral Access maXimal Reduction of occupancY support Area),
a system to automate the mechanical search for occluded objects on shelves. For
such lateral access environments, LAX-RAY couples a perception pipeline
predicting a target object occupancy support distribution with a mechanical
search policy that sequentially selects occluding objects to push to the side
to reveal the target as efficiently as possible. Within the context of extruded
polygonal objects and a stationary target with a known aspect ratio, we explore
three lateral access search policies: Distribution Area Reduction (DAR),
Distribution Entropy Reduction (DER), and Distribution Entropy Reduction over
Multiple Time Steps (DER-MT) utilizing the support distribution and prior
information. We evaluate these policies using the First-Order Shelf Simulator
(FOSS) in which we simulate 800 random shelf environments of varying
difficulty, and in a physical shelf environment with a Fetch robot and an
embedded PrimeSense RGBD Camera. Average simulation results of 87.3% success
rate demonstrate better performance of DER-MT with 2 prediction steps. When
deployed on the robot, results show a success rate of at least 80% for all
policies, suggesting that LAX-RAY can efficiently reveal the target object in
reality. Both results show significantly better performance of the three
proposed policies compared to a baseline policy with uniform probability
distribution assumption in non-trivial cases, showing the importance of
distribution prediction. Code, videos, and supplementary material can be found
at https://sites.google.com/berkeley.edu/lax-ray.
