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dc.contributor.advisorBoots, Byron
dc.contributor.authorMukadam, Mustafa
dc.date.accessioned2019-08-21T13:51:39Z
dc.date.available2019-08-21T13:51:39Z
dc.date.created2019-08
dc.date.issued2019-07-25
dc.date.submittedAugust 2019
dc.identifier.urihttp://hdl.handle.net/1853/61714
dc.description.abstractThe ability to generate motions that accomplish desired tasks is fundamental to any robotic system. Robots must be able to generate such motions in a safe and feasible manner, sufficiently quickly, and in dynamic and uncertain environments. In addressing these problems, there exists a dichotomy between traditional methods and modern learning-based approaches. Often both of these paradigms exhibit complementary strengths and weaknesses, for example, while the former are interpretable and integrate prior knowledge, the latter are data-driven and flexible to design. In this thesis, I present two techniques for robot motion generation that exploit structure to bridge this gap and leverage the best of both worlds to efficiently find solutions in real-time. The first technique is a planning as inference framework that encodes structure through probabilistic graphical models, and the second technique is a reactive policy synthesis framework that encodes structure through task-map trees. Within both frameworks, I present two strategies that use said structure as a canvas to incorporate learning in a manner that is generalizable and interpretable while maintaining constraints like safety even during learning.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectMotion planning
dc.subjectMachine learning
dc.titleStructured learning and inference for robot motion generation
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberDellaert, Frank
dc.contributor.committeeMemberChernova, Sonia
dc.contributor.committeeMemberTheodorou, Evangelos
dc.contributor.committeeMemberRatliff, Nathan
dc.date.updated2019-08-21T13:51:39Z


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