Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable of reliably sorting large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.

Haoran Song, Joshua A. Haustein, Weihao Yuan, Kaiyu Hang, Michael Yu Wang, Danica Kragic, Johannes A. Stork. “Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
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