Title:
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
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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