Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting
Haoran Song, Joshua A. Haustein, Weihao Yuan, Kaiyu Hang, Michael Yu Wang, Danica Kragić, Johannes A. Stork
Abstract
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.
Topics & Concepts
SortingMonte Carlo tree searchComputer scienceTask (project management)Monte Carlo methodTree (set theory)Object (grammar)Regular polygonHeuristicAlgorithmArtificial intelligenceMathematicsCombinatoricsEngineeringStatisticsGeometrySystems engineeringRobotic Path Planning AlgorithmsRobotics and Sensor-Based LocalizationOptimization and Search Problems