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Multi-Object Rearrangement with Monte Carlo Tree Search: A Case Study on Planar Nonprehensile Sorting
Robotics Institute, Hong Kong University of Science and Technology, Hong Kong, China.
Centre for Autonomous Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
Robotics Institute, Hong Kong University of Science and Technology, Hong Kong, China.
Department of Mechanical Engineering and Material Science, Yale University, New Haven Connecticut, USA.
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

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 sorting tasks and observe its effectiveness in reliably sorting up to 40 convex objects. In addition, we observe that the algorithm is capable to also sort non-convex objects, as well as convex objects in the presence of immovable obstacles.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-78690OAI: oai:DiVA.org:oru-78690DiVA, id: diva2:1379584
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved

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Multi-Object Rearrangement with Monte Carlo Tree Search

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Stork, Johannes Andreas

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