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dc.contributor.authorDyrstad, Jonatan Sjølund
dc.contributor.authorØye, Elling Ruud
dc.contributor.authorStahl, Annette
dc.contributor.authorMathiassen, John Reidar Bartle
dc.date.accessioned2019-01-28T12:28:38Z
dc.date.available2019-01-28T12:28:38Z
dc.date.created2018-09-21T10:13:15Z
dc.date.issued2018
dc.identifier.issn2153-0858
dc.identifier.urihttp://hdl.handle.net/11250/2582626
dc.description.abstractWe teach a real robot to grasp real fish, by training a virtual robot exclusively in virtual reality. Our approach implements robot imitation learning from a human supervisor in virtual reality. A deep 3D convolutional neural network computes grasps from a 3D occupancy grid obtained from depth imaging at multiple viewpoints. In virtual reality, a human supervisor can easily and intuitively demonstrate examples of how to grasp an object, such as a fish. From a few dozen of these demonstrations, we use domain randomization to generate a large synthetic training data set consisting of 100 000 example grasps of fish. Using this data set for training purposes, the network is able to guide a real robot and gripper to grasp real fish with good success rates. The newly proposed domain randomization approach constitutes the first step in how to efficiently perform robot imitation learning from a human supervisor in virtual reality in a way that transfers well to the real world.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleTeaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Realitynb_NO
dc.title.alternativeTeaching a Robot to Grasp Real Fish by Imitation Learning from a Human Supervisor in Virtual Realitynb_NO
dc.typeJournal articlenb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.journalIEEE International Conference on Intelligent Robots and Systems. Proceedingsnb_NO
dc.identifier.doi10.1109/IROS.2018.8593954
dc.identifier.cristin1611909
dc.relation.projectNorges forskningsråd: 262900nb_NO
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedfalse
cristin.fulltextpreprint
cristin.qualitycode1


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