Learning robotic manipulation of natural materials with variable properties for construction tasks

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buir.contributor.authorOğuz, Özgür S.
buir.contributor.orcidOğuz, Özgür S.|0000-0001-8723-1837
dc.citation.epage5756en_US
dc.citation.issueNumber2en_US
dc.citation.spage5749en_US
dc.citation.volumeNumber7en_US
dc.contributor.authorKalousdian, N. K.
dc.contributor.authorLochnicki, G.
dc.contributor.authorHartmann, V. N.
dc.contributor.authorLeder, S.
dc.contributor.authorOğuz, Özgür S.
dc.contributor.authorMenges, A.
dc.contributor.authorToussaint, M.
dc.date.accessioned2023-02-28T13:19:45Z
dc.date.available2023-02-28T13:19:45Z
dc.date.issued2022-03-15
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThe introduction of robotics and machine learning to architectural construction is leading to more efficient construction practices. So far, robotic construction has largely been implemented on standardized materials, conducting simple, predictable, and repetitive tasks. We present a novel mobile robotic system and corresponding learning approach that takes a step towards assembly of natural materials with anisotropic mechanical properties for more sustainable architectural construction. Through experiments both in simulation and in the real world, we demonstrate a dynamically adjusted curriculum and randomization approach for the problem of learning manipulation tasks involving materials with biological variability, namely bamboo. Using our approach, robots are able to transport bamboo bundles and reach to goal-positions during the assembly of bamboo structures.en_US
dc.identifier.doi10.1109/LRA.2022.3159288en_US
dc.identifier.eissn2377-3766
dc.identifier.urihttp://hdl.handle.net/11693/111943
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/LRA.2022.3159288en_US
dc.source.titleIEEE Robotics and Automation Lettersen_US
dc.subjectAI-enabled roboticsen_US
dc.subjectHardware-softwareen_US
dc.subjectIntegration in roboticsen_US
dc.subjectRobotics and automation in constructionen_US
dc.titleLearning robotic manipulation of natural materials with variable properties for construction tasksen_US
dc.typeArticleen_US
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