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dc.contributor.authorShah, Ankit Jayesh
dc.contributor.authorShah, Julie A
dc.date.accessioned2021-11-04T14:43:51Z
dc.date.available2021-11-04T14:43:51Z
dc.date.issued2020-03
dc.identifier.urihttps://hdl.handle.net/1721.1/137332
dc.description.abstract© 2020 ACM. Imagine a future where a domestic robot ships with a state-of-theart learning from demonstrations (LfD) system to learn household tasks. You would like the robot to set the dinner-table for you when you get home at dinner time. After you demonstrate how to set the dinner table a couple of times. Would you be confident that robot will not try to place the saucer on top of the cup, or finish as much of the task as possible if an object was missing?en_US
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/3371382.3377437en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleInteractive Robot Training for Temporal Tasksen_US
dc.typeArticleen_US
dc.identifier.citationShah, Ankit Jayesh and Shah, Julie A. 2020. "Interactive Robot Training for Temporal Tasks." ACM/IEEE International Conference on Human-Robot Interaction.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.relation.journalACM/IEEE International Conference on Human-Robot Interactionen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-05-04T14:51:20Z
dspace.orderedauthorsShah, A; Shah, Jen_US
dspace.date.submission2021-05-04T14:51:21Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusPublication Information Neededen_US


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