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dc.contributor.authorMa, Daolin
dc.contributor.authorRodriguez Garcia, Alberto
dc.date.accessioned2019-01-11T19:23:01Z
dc.date.available2019-01-11T19:23:01Z
dc.date.issued2018-06
dc.identifier.issn2377-3766
dc.identifier.issn2377-3774
dc.identifier.urihttp://hdl.handle.net/1721.1/120004
dc.description.abstractFriction plays a key role in manipulating objects. Most of what we do with our hands, and most of what robots do with their grippers, is based on the ability to control frictional forces. This paper aims to better understand the variability and predictability of planar friction. In particular, we focus on the analysis of a recent dataset on planar pushing by Yu et al. [1] devised to create a data-driven footprint of planar friction. We show in this paper how we can explain a significant fraction of the observed unconventional phenomena, e.g., stochasticity and multi-modality, by combining the effects of material non-homogeneity, anisotropy of friction and biases due to data collection dynamics, hinting that the variability is explainable but inevitable in practice. We introduce an anisotropic friction model and conduct simulation experiments comparing with more standard isotropic friction models. The anisotropic friction between object and supporting surface results in convergence of initial condition during the automated data collection. Numerical results confirm that the anisotropic friction model explains the bias in the dataset and the apparent stochasticity in the outcome of a push. The fact that the data collection process itself can originate biases in the collected datasets, resulting in deterioration of trained models, calls attention to the data collection dynamics.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/LRA.2018.2851026en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleFriction Variability in Planar Pushing Data: Anisotropic Friction and Data-Collection Biasen_US
dc.typeArticleen_US
dc.identifier.citationMa, Daolin, and Alberto Rodriguez. “Friction Variability in Planar Pushing Data: Anisotropic Friction and Data-Collection Bias.” IEEE Robotics and Automation Letters 3, no. 4 (October 2018): 3232–3239.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorMa, Daolin
dc.contributor.mitauthorRodriguez Garcia, Alberto
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.updated2018-12-17T18:27:25Z
dspace.orderedauthorsMa, Daolin; Rodriguez, Albertoen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1119-4512
mit.licenseOPEN_ACCESS_POLICYen_US


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