Title:
Structured learning and inference for robot motion generation
Structured learning and inference for robot motion generation
| dc.contributor.advisor | Boots, Byron | |
| dc.contributor.author | Mukadam, Mustafa | |
| dc.contributor.committeeMember | Dellaert, Frank | |
| dc.contributor.committeeMember | Chernova, Sonia | |
| dc.contributor.committeeMember | Theodorou, Evangelos | |
| dc.contributor.committeeMember | Ratliff, Nathan | |
| dc.contributor.department | Electrical and Computer Engineering | |
| dc.date.accessioned | 2019-08-21T13:51:39Z | |
| dc.date.available | 2019-08-21T13:51:39Z | |
| dc.date.created | 2019-08 | |
| dc.date.issued | 2019-07-25 | |
| dc.date.submitted | August 2019 | |
| dc.date.updated | 2019-08-21T13:51:39Z | |
| dc.description.abstract | The 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.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/61714 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Motion planning | |
| dc.subject | Machine learning | |
| dc.title | Structured learning and inference for robot motion generation | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | School of Electrical and Computer Engineering | |
| local.contributor.corporatename | College of Engineering | |
| relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
| relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 | |
| thesis.degree.level | Doctoral |