  Stacking increases storage efficiency in shelves, but the lack of visibility
and accessibility makes the mechanical search problem of revealing and
extracting target objects difficult for robots. In this paper, we extend the
lateral-access mechanical search problem to shelves with stacked items and
introduce two novel policies -- Distribution Area Reduction for Stacked Scenes
(DARSS) and Monte Carlo Tree Search for Stacked Scenes (MCTSSS) -- that use
destacking and restacking actions. MCTSSS improves on prior lookahead policies
by considering future states after each potential action. Experiments in 1200
simulated and 18 physical trials with a Fetch robot equipped with a blade and
suction cup suggest that destacking and restacking actions can reveal the
target object with 82--100% success in simulation and 66--100% in physical
experiments, and are critical for searching densely packed shelves. In the
simulation experiments, both policies outperform a baseline and achieve similar
success rates but take more steps compared with an oracle policy that has full
state information. In simulation and physical experiments, DARSS outperforms
MCTSSS in median number of steps to reveal the target, but MCTSSS has a higher
success rate in physical experiments, suggesting robustness to perception
noise. See https://sites.google.com/berkeley.edu/stax-ray for supplementary
material.
