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dc.contributor.authorCosti, Leone
dc.contributor.authorMaiolino, Perla
dc.contributor.authorIida, Fumiya
dc.date.accessioned2022-06-10T23:31:02Z
dc.date.available2022-06-10T23:31:02Z
dc.date.issued2022
dc.identifier.issn2296-9144
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338015
dc.description.abstractThe mechanical properties of a sensor strongly affect its tactile sensing capabilities. By exploiting tactile filters, mechanical structures between the sensing unit and the environment, it is possible to tune the interaction dynamics with the surrounding environment. But how can we design a good tactile filter? Previously, the role of filters' geometry and stiffness on the quality of the tactile data has been the subject of several studies, both implementing static filters and adaptable filters. State-of-the-art works on online adaptive stiffness highlight a crucial role of the filters' mechanical behavior in the structure of the recorded tactile data. However, the relationship between the filter's and the environment's characteristics is still largely unknown. We want to show the effect of the environment's mechanical properties on the structure of the acquired tactile data and the performance of a classification task while testing a wide range of static tactile filters. Moreover, we fabricated the filters using four materials commonly exploited in soft robotics, to merge the gap between tactile sensing and robotic applications. We collected data from the interaction with a standard set of twelve objects of different materials, shapes, and textures, and we analyzed the effect of the filter's material on the structure of such data and the performance of nine common machine learning classifiers, both considering the overall test set and the three individual subsets made by all objects of the same material. We showed that depending on the material of the test objects, there is a drastic change in the performance of the four tested filters, and that the filter that matches the mechanical properties of the environment always outperforms the others.
dc.description.sponsorshipThis work was supported by the SMART project, European Union's Horizon 2020 research and innovation under the Marie Sklodowska-Curie (grant agreement ID 860108).
dc.publisherFrontiers Media
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleHow the Environment Shapes Tactile Sensing: Understanding the Relationship Between Tactile Filters and Surrounding Environment.
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-06-10T12:38:32Z
prism.publicationNameFront Robot AI
dc.identifier.doi10.17863/CAM.85420
dcterms.dateAccepted2022-06-09
rioxxterms.versionofrecord10.3389/frobt.2022.930405
rioxxterms.versionAM
dc.contributor.orcidCosti, Leone [0000-0001-6023-0228]
dc.identifier.eissn2296-9144
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (860108)
cam.issuedOnline2022-07-11
cam.orpheus.successWed Aug 03 09:45:49 BST 2022 - Embargo updated
cam.orpheus.counter5
cam.depositDate2022-06-10
pubs.licence-identifierapollo-deposit-licence-2-1
pubs.licence-display-nameApollo Repository Deposit Licence Agreement
rioxxterms.freetoread.startdate2022-12-31


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International