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dc.contributor.authorChamzas, Constantinos
Quintero-Peña, Carlos
Kingston, Zachary
Orthey, Andreas
Rakita, Daniel
Gleicher, Michael
Toussaint, Marc
Kavraki, Lydia E.
dc.date.accessioned 2022-08-15T15:11:12Z
dc.date.available 2022-08-15T15:11:12Z
dc.date.issued 2022
dc.identifier.citation Chamzas, Constantinos, Quintero-Peña, Carlos, Kingston, Zachary, et al.. "MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets." IEEE Robotics and Automation Letters, 7, no. 2 (2022) IEEE: 882-889. https://doi.org/10.1109/LRA.2021.3133603.
dc.identifier.urihttps://hdl.handle.net/1911/113097
dc.description.abstract Recently, there has been a wealth of development in motion planning for robotic manipulation—new motion planners are continuously proposed, each with their own unique strengths and weaknesses. However, evaluating new planners is challenging and researchers often create their own ad-hoc problems for benchmarking, which is time-consuming, prone to bias, and does not directly compare against other state-of-the-art planners. We present MotionBenchMaker , an open-source tool to generate benchmarking datasets for realistic robot manipulation problems. MotionBenchMaker is designed to be an extensible, easy-to-use tool that allows users to both generate datasets and benchmark them by comparing motion planning algorithms. Empirically, we show the benefit of using MotionBenchMaker as a tool to procedurally generate datasets which helps in the fair evaluation of planners. We also present a suite of 40 prefabricated datasets, with 5 different commonly used robots in 8 environments, to serve as a common ground to accelerate motion planning research.
dc.language.iso eng
dc.publisher IEEE
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by IEEE.
dc.title MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets
dc.type Journal article
dc.citation.journalTitle IEEE Robotics and Automation Letters
dc.citation.volumeNumber 7
dc.citation.issueNumber 2
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1109/LRA.2021.3133603
dc.type.publication post-print
dc.citation.firstpage 882
dc.citation.lastpage 889


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