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dc.contributor.authorNikolaidis, Stefanos
dc.contributor.authorRossano, Gregory
dc.contributor.authorMartinez, Carlos
dc.contributor.authorFuhlbrigge, Thomas
dc.contributor.authorLasota, Przemyslaw Andrzej
dc.contributor.authorShah, Julie A.
dc.date.accessioned2015-05-21T14:28:03Z
dc.date.available2015-05-21T14:28:03Z
dc.date.issued2013-10
dc.identifier.isbn978-1-4799-1173-8
dc.identifier.urihttp://hdl.handle.net/1721.1/97048
dc.description.abstractNew industrial robotic systems that operate in the same physical space as people highlight the emerging need for robots that can integrate seamlessly into human group dynamics. In this paper we build on our prior investigation, which evaluates the convergence of a robot computational teaming model and a human teammate's mental model, by computing the entropy rate of the Markov chain. We present and analyze the six out of thirty-six human trials where the human participant switched execution strategies while working with the robot. We conduct a post-hoc analysis of this dataset and show that the entropy rate appears to be sensitive to changes in the human strategy and reflects the resulting increase in uncertainty about the human next actions. We propose that these results provide first support that entropy rate may be used as a component of dynamic risk assessment, to generate risk-aware robot motions and action selections.en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ISR.2013.6695625en_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.titleHuman-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint actionen_US
dc.typeArticleen_US
dc.identifier.citationNikolaidis, Stefanos, Przemyslaw Lasota, Gregory Rossano, Carlos Martinez, Thomas Fuhlbrigge, and Julie Shah. “Human-Robot Collaboration in Manufacturing: Quantitative Evaluation of Predictable, Convergent Joint Action.” IEEE ISR 2013 (October 2013).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.mitauthorNikolaidis, Stefanosen_US
dc.contributor.mitauthorLasota, Przemyslaw Andrzejen_US
dc.contributor.mitauthorShah, Julie A.en_US
dc.relation.journalProceedings of the 2013 IEEE 44th International Symposium on Roboticsen_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
dspace.orderedauthorsNikolaidis, Stefanos; Lasota, Przemyslaw; Rossano, Gregory; Martinez, Carlos; Fuhlbrigge, Thomas; Shah, Julieen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1761-221X
dc.identifier.orcidhttps://orcid.org/0000-0003-1338-8107
mit.licenseOPEN_ACCESS_POLICYen_US


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