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dc.contributor.authorMaurice, Pauline
dc.contributor.authorHuber, Meghan E
dc.contributor.authorHogan, Neville
dc.contributor.authorSternad, Dagmar
dc.date.accessioned2018-12-03T17:45:20Z
dc.date.available2018-12-03T17:45:20Z
dc.date.issued2018-01
dc.identifier.issn2377-3766
dc.identifier.issn2377-3774
dc.identifier.urihttp://hdl.handle.net/1721.1/119394
dc.description.abstractPhysical human-robot collaboration is becoming more common, both in industrial and service robotics. Cooperative execution of a task requires intuitive and efficient interaction between both actors. For humans, this means being able to predict and adapt to robot movements. Given that natural human movement exhibits several robust features, we examined whether human-robot physical interaction is facilitated when these features are considered in robot control. The present study investigated how humans adapt to biological and non-biological velocity patterns in robot movements. Participants held the end-effector of a robot that traced an elliptic path with either biological (two-thirds power law) or non-biological velocity profiles. Participants were instructed to minimize the force applied on the robot end-effector. Results showed that the applied force was significantly lower when the robot moved with a biological velocity pattern. With extensive practice and enhanced feedback, participants were able to decrease their force when following a non-biological velocity pattern, but never reached forces below those obtained with the 2/3 power law profile. These results suggest that some robust features observed in natural human movements are also a strong preference in guided movements. Therefore, such features should be considered in human-robot physical collaboration.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH-R01-HD087089)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637854)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). National Robotics Initiative (NSF-NRI 1637824)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER 1548514)en_US
dc.description.sponsorshipNational Science Foundation (U.S.). EArly-concept Grants for Exploratory Research (NSF-EAGER 1548501)en_US
dc.description.sponsorshipEric P. and Evelyn E. Newman Funden_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/LRA.2017.2737048en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcePMCen_US
dc.titleVelocity-Curvature Patterns Limit Human–Robot Physical Interactionen_US
dc.typeArticleen_US
dc.identifier.citationMaurice, Pauline, Meghan E. Huber, Neville Hogan, and Dagmar Sternad. “Velocity-Curvature Patterns Limit Human–Robot Physical Interaction.” IEEE Robotics and Automation Letters 3, no. 1 (January 2018): 249–256.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorHuber, Meghan E
dc.contributor.mitauthorHogan, Neville
dc.contributor.mitauthorSternad, Dagmar
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.updated2018-11-30T19:46:47Z
dspace.orderedauthorsMaurice, Pauline; Huber, Meghan E.; Hogan, Neville; Sternad, Dagmaren_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0001-5366-2145
mit.licenseOPEN_ACCESS_POLICYen_US


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