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dc.contributor.authorMoore, B.
dc.contributor.authorÖztop, Erhan
dc.date.accessioned2012-08-14T13:51:07Z
dc.date.available2012-08-14T13:51:07Z
dc.date.issued2012-03
dc.identifier.issn0921-8890
dc.identifier.urihttp://hdl.handle.net/10679/229
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0921889011001758
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.en_US
dc.description.abstractA major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ballswapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object.en_US
dc.description.sponsorshipthe Global COE Program, Center of Human-Friendly Robotics Based on Cognitive Neuroscience at the Ministry of Education, Culture, Sports, Science and Technology, Japan.
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofRobotics and Autonomous Systems
dc.rightsrestrictedAccess
dc.titleRobotic grasping and manipulation through human visuomotor learningen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-3051-6038 & YÖK ID 45227) Öztop, Erhan
dc.contributor.ozuauthorÖztop, Erhan
dc.identifier.volume60
dc.identifier.issue3
dc.identifier.startpage441
dc.identifier.endpage451
dc.identifier.wosWOS:000300866800012
dc.identifier.doi10.1016/j.robot.2011.09.002
dc.subject.keywordsBody schemaen_US
dc.subject.keywordsSkill synthesisen_US
dc.subject.keywordsHand controlen_US
dc.subject.keywordsRobot graspingen_US
dc.subject.keywordsRobot manipulationen_US
dc.identifier.scopusSCOPUS:2-s2.0-84856013753
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institutional Academic Staff


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