Investigation into the Impact of Object intrinsic Features on Grasp Affordances

dc.contributor.advisorMoussa, Medhat
dc.contributor.authorCabral, Ian
dc.date.accessioned2020-05-04T13:28:34Z
dc.date.available2020-05-04T13:28:34Z
dc.date.copyright2020-05
dc.date.created2020-04-21
dc.date.issued2020-05-04
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Applied Scienceen_US
dc.degree.programmeEngineeringen_US
dc.description.abstractRobotic grasping systems often rely on visual observations to drive the grasping process, where the robot must be able to detect and localize an object, extract features relevant to the task, and then combine this information to plan a manipulation strategy. But what happens when some of the most impactful features are not observed by the robot? Without context on an object’s center of mass, for example, a robot may make assumptions such as uniform density that do not hold, and which may in turn guide the robot into perceiving a sub-optimal set of grasping configurations. In this work, we examine how having prior knowledge of an object’s intrinsic properties influences the task of dense grasp affordance prediction. We investigate a simple, constrained grasping task where object properties heavily regulate the space of successful grasps, and further evaluate how learning is affected when generalizing across unseen weight configurations and unseen object shapes.en_US
dc.identifier.urihttp://hdl.handle.net/10214/17894
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/*
dc.subjectAffordancesen_US
dc.subjectGraspingen_US
dc.subjectDeep Learningen_US
dc.titleInvestigation into the Impact of Object intrinsic Features on Grasp Affordancesen_US
dc.typeThesisen_US

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Investigation into the Impact of Object intrinsic Features on Grasp Affordances