Title:
Motion Preference Learning
Motion Preference Learning
dc.contributor.author | Kingston, Peter | en_US |
dc.contributor.author | Egerstedt, Magnus B. | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | en_US |
dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | en_US |
dc.date.accessioned | 2011-09-30T17:30:13Z | |
dc.date.available | 2011-09-30T17:30:13Z | |
dc.date.issued | 2011-06 | |
dc.description | © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. | en_US |
dc.description | Presented at the American Control Conference, San Francisco, CA, June 2011. | en_US |
dc.description.abstract | In order to control systems to meet subjective criteria, one would like to construct objective functions that accurately represent human preferences. To do this, we develop robust estimators based on convex optimization that, given empirical, pairwise comparisons between motions, produce both (1) objective functions that are compatible with the expressed preferences, and (2) global optimizers (i.e., “best motions”) for these functions. The approach is demonstrated with an example in which human and synthetic motions are compared. | en_US |
dc.identifier.citation | P. Kingston and M. Egerstedt. Motion Preference Learning. American Control Conference, San Francisco, CA, June 2011. | en_US |
dc.identifier.isbn | 978-1-4577-0080-4 | |
dc.identifier.issn | 0743-1619 | |
dc.identifier.uri | http://hdl.handle.net/1853/41702 | |
dc.language.iso | en_US | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.publisher.original | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Human motion | en_US |
dc.subject | Control systems | en_US |
dc.subject | Synthetic motion | en_US |
dc.subject | Support vector machines | en_US |
dc.title | Motion Preference Learning | en_US |
dc.type | Text | |
dc.type.genre | Proceedings | |
dc.type.genre | Post-print | |
dspace.entity.type | Publication | |
local.contributor.author | Egerstedt, Magnus B. | |
local.contributor.corporatename | School of Electrical and Computer Engineering | |
local.contributor.corporatename | College of Engineering | |
relation.isAuthorOfPublication | dd4872d3-2e0d-435d-861d-a61559d2bcb6 | |
relation.isOrgUnitOfPublication | 5b7adef2-447c-4270-b9fc-846bd76f80f2 | |
relation.isOrgUnitOfPublication | 7c022d60-21d5-497c-b552-95e489a06569 |
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