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dc.contributor.authorBobu, Andreea
dc.contributor.authorPeng, Andi
dc.contributor.authorAgrawal, Pulkit
dc.contributor.authorShah, Julie A
dc.contributor.authorDragan, Anca D.
dc.date.accessioned2024-04-03T18:51:02Z
dc.date.available2024-04-03T18:51:02Z
dc.date.issued2024-03-11
dc.identifier.isbn979-8-4007-0322-5
dc.identifier.urihttps://hdl.handle.net/1721.1/154054
dc.descriptionHRI ’24, March 11–14, 2024, Boulder, CO, USAen_US
dc.description.abstractTo act in the world, robots rely on a representation of salient task aspects: for example, to carry a coffee mug, a robot may consider movement efficiency or mug orientation in its behavior. However, if we want robots to act for and with people, their representations must not be just functional but also reflective of what humans care about, i.e. they must be aligned. We observe that current learning approaches suffer from representation misalignment, where the robot's learned representation does not capture the human's representation. We suggest that because humans are the ultimate evaluator of robot performance, we must explicitly focus our efforts on aligning learned representations with humans, in addition to learning the downstream task. We advocate that current representation learning approaches in robotics should be studied from the perspective of how well they accomplish the objective of representation alignment. We mathematically define the problem, identify its key desiderata, and situate current methods within this formalism. We conclude by suggesting future directions for exploring open challenges.en_US
dc.publisherACMen_US
dc.relation.isversionof10.1145/3610977.3634987en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleAligning Human and Robot Representationsen_US
dc.typeArticleen_US
dc.identifier.citationBobu, Andreea, Peng, Andi, Agrawal, Pulkit, Shah, Julie A and Dragan, Anca D. 2024. "Aligning Human and Robot Representations."
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2024-04-01T07:46:34Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2024-04-01T07:46:34Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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