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Stacking increases storage efficiency in shelves, but the lack of visibilityand accessibility makes the mechanical search problem of revealing andextracting target objects difficult for robots. In this paper, we extend thelateral-access mechanical search problem to shelves with stacked items andintroduce two novel policies -- Distribution Area Reduction for Stacked Scenes(DARSS) and Monte Carlo Tree Search for Stacked Scenes (MCTSSS) -- that usedestacking and restacking actions. MCTSSS improves on prior lookahead policiesby considering future states after each potential action. Experiments in 1200simulated and 18 physical trials with a Fetch robot equipped with a blade andsuction cup suggest that destacking and restacking actions can reveal thetarget object with 82--100% success in simulation and 66--100% in physicalexperiments, and are critical for searching densely packed shelves. In thesimulation experiments, both policies outperform a baseline and achieve similarsuccess rates but take more steps compared with an oracle policy that has fullstate information. In simulation and physical experiments, DARSS outperformsMCTSSS in median number of steps to reveal the target, but MCTSSS has a highersuccess rate in physical experiments, suggesting robustness to perceptionnoise. See https://sites.google.com/berkeley.edu/stax-ray for supplementarymaterial.

Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects