Abstract
Although stacking objects increases shelves storage efficiency, the lack of visibility and accessibility makes the mechanical search problem of revealing and extracting a target object 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 three future states after each potential action. Experiments with 3600 simulated and 18 physical trials with a Fetch robot equipped with a blade and suction cup suggest that these policies can reveal the target object with 82–100% success in simulation outperforming the baseline by up to 66%, and can achieve 67–100% success in physical experiments. DARSS outperforms MCTSSS on median number of steps to reveal the target, but MCTSSS has a higher success rate in physical experiments, suggesting its robustness to perception noise. See https://sites.google.com/berkeley.edu/stax-ray for supplementary material.
H. Huang and L. Fu—Equal contribution.
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Acknowledgements
This research was performed at the AUTOLAB at UC Berkeley in affiliation with the Berkeley AI Research (BAIR) Lab, and the CITRIS “People and Robots” (CPAR) Initiative. The authors were supported in part by donations from Google, Toyota Research Institute, Autodesk, Siemens and by equipment grants from PhotoNeo and NVidia.
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Huang, H. et al. (2023). Mechanical Search on Shelves with Efficient Stacking and Destacking of Objects. In: Billard, A., Asfour, T., Khatib, O. (eds) Robotics Research. ISRR 2022. Springer Proceedings in Advanced Robotics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-25555-7_14
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