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Action-conditioned convolutional future regression models for robot imitation learning

  • Indiana University Bloomington

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Based on what is seen (i.e. visual input), humans are able to visually predict (i.e. regress) what the scene will look like after taking a certain action. Further, humans are able to take advantage of such predictions to select optimal actions for the task they are working on. Using example videos, robots can also learn to visually imagine the future consequence of taking an action. This can be viewed as learning a function mapping a raw image frame (conditioned on a particular action) to the future image frame. Once learned, the future regression function can be combined with an action policy learning framework (e.g. reinforcement or imitation learning), enabling better robot action learning for given tasks.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages2116-2118
Number of pages3
ISBN (Electronic)9781538661000
DOIs
StatePublished - Dec 13 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2018-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Country/TerritoryUnited States
CitySalt Lake City
Period06/18/1806/22/18

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