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dc.contributor.authorLi, Xiao
dc.contributor.authorRosman, Guy
dc.contributor.authorGilitschenski, Igor
dc.contributor.authorAraki, Brandon
dc.contributor.authorVasile, Cristian-Ioan
dc.contributor.authorKaraman, Sertac
dc.contributor.authorRus, Daniela
dc.date.accessioned2022-07-26T16:23:59Z
dc.date.available2022-07-26T16:23:59Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/144054
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/LRA.2021.3135940en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titleLearning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)en_US
dc.typeArticleen_US
dc.identifier.citationLi, Xiao, Rosman, Guy, Gilitschenski, Igor, Araki, Brandon, Vasile, Cristian-Ioan et al. 2022. "Learning an Explainable Trajectory Generator Using the Automaton Generative Network (AGN)." IEEE Robotics and Automation Letters, 7 (2).
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
dc.relation.journalIEEE Robotics and Automation Lettersen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-07-26T16:21:27Z
dspace.orderedauthorsLi, X; Rosman, G; Gilitschenski, I; Araki, B; Vasile, C-I; Karaman, S; Rus, Den_US
dspace.date.submission2022-07-26T16:21:29Z
mit.journal.volume7en_US
mit.journal.issue2en_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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