{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:33:59Z","timestamp":1785249239888,"version":"3.55.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Slip detection is to recognize whether an object remains stable during grasping, which can significantly enhance manipulation dexterity. In this study, we explore slip detection for five-finger robotic hands being capable of performing various grasp types, and detect slippage across all five fingers as a whole rather than concentrating on individual fingertips. First, we constructed a dataset collected during the grasping of common objects from daily life across six grasp types, comprising more than 200\u202fk data points. Second, according to the principle of deep double descent, we designed a lightweight universal slip detection convolutional network for different grasp types (USDConvNet-DG) to classify grasp states (no-touch, slipping, and stable grasp). By combining frequency with time domain features, the network achieves a computation time of only 1.26\u202fms and an average accuracy of over 97% on both the validation and test datasets, demonstrating strong generalization capabilities. Furthermore, we validated the proposed USDConvNet-DG in real-time grasp force adjustment in real-world scenarios, showing that it can effectively improve the stability and reliability of robotic manipulation.<\/jats:p>","DOI":"10.3389\/fnbot.2025.1478758","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T07:32:28Z","timestamp":1738740748000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Universal slip detection of robotic hand with tactile sensing"],"prefix":"10.3389","volume":"19","author":[{"given":"Chuangri","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeqi","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyang","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Li","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"ref1","volume-title":"Grant's atlas of anatomy","author":"Agur","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1109\/TMECH.2008.918483","article-title":"Development and experimental analysis of a soft compliant tactile microsensor for anthropomorphic artificial hand","volume":"13","author":"Beccai","year":"2008","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref3","first-page":"191","article-title":"Self-supervised learning of object slippage: an LSTM model trained on low-cost tactile sensors","author":"Begalinova","year":"2022"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"9049","DOI":"10.1109\/JSEN.2018.2868340","article-title":"Tactile sensors for friction estimation and incipient slip detection\u2014toward dexterous robotic manipulation: a review","volume":"18","author":"Chen","year":"2018","journal-title":"IEEE Sensors J."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"13691","DOI":"10.1109\/TNNLS.2023.3270579","article-title":"Learning-based slip detection for dexterous manipulation using GelStereo sensing","volume":"35","author":"Cui","year":"2024","journal-title":"IEEE Trans. Neural Net. Learn. Syst."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.1109\/LRA.2020.3010720","article-title":"Self-attention based visual-tactile fusion learning for predicting grasp outcomes","volume":"5","author":"Cui","year":"2020","journal-title":"IEEE Robo. Autom. Lett."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.3390\/s20041050","article-title":"Grasping force control of multi-fingered robotic hands through tactile sensing for object stabilization","volume":"20","author":"Deng","year":"2020","journal-title":"Sensors"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"3718","DOI":"10.1109\/TIE.2016.2643603","article-title":"Wavelet transformation-based fuzzy reflex control for prosthetic hands to prevent slip","volume":"64","author":"Deng","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0165-1684(90)90158-U","article-title":"Fast Fourier transforms: a tutorial review and a state of the art","volume":"19","author":"Duhamel","year":"1990","journal-title":"Signal Process."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/THMS.2015.2470657","article-title":"The GRASP taxonomy of human grasp types","volume":"46","author":"Feix","year":"2016","journal-title":"IEEE Trans. Human-Machine Syst."},{"key":"ref12","first-page":"1","article-title":"A multimodal pipeline for grasping fabrics from flat surfaces with tactile slip and fall detection","author":"Fiedler","year":"2023"},{"key":"ref13","first-page":"1","article-title":"TactileGCN: a graph convolutional network for predicting grasp stability with tactile sensors","author":"Garcia-Garcia","year":"2019"},{"key":"ref14","first-page":"570","article-title":"Learning to detect slip with barometric tactile sensors and a temporal convolutional neural network","author":"Grover","year":"2022"},{"key":"ref15","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1109\/ROBOT.1996.509205","article-title":"Slip detection by tactile sensors: algorithms and experimental results","author":"Holweg","year":"1996"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1109\/TRO.2020.3031245","article-title":"Slip detection for grasp stabilization with a multifingered tactile robot hand","volume":"37","author":"James","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"3340","DOI":"10.1109\/LRA.2018.2852797","article-title":"Slip detection with a biomimetic tactile sensor","volume":"3","author":"James","year":"2018","journal-title":"IEEE Robo. Autom. Lett."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/nrn2621","article-title":"Coding and use of tactile signals from the fingertips in object manipulation tasks","volume":"10","author":"Johansson","year":"2009","journal-title":"Nat. Rev. Neurosci."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1113\/jphysiol.1979.sp012619","article-title":"Tactile sensibility in the human hand: relative and absolute densities of four types of mechanoreceptive units in glabrous skin","volume":"286","author":"Johansson","year":"1979","journal-title":"J. Physiol."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1007\/BF00237997","article-title":"Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects","volume":"56","author":"Johansson","year":"1984","journal-title":"Exp. Brain Res."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1109\/3516.868914","article-title":"Slip detection and control using tactile and force sensors","volume":"5","author":"Melchiorri","year":"2000","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref24","first-page":"875","article-title":"Tactile grasp stability classification based on graph convolutional networks","author":"Mi","year":""},{"key":"ref25","doi-asserted-by":"publisher","first-page":"124003","DOI":"10.1088\/1742-5468\/ac3a74","article-title":"Deep double descent: where bigger models and more data hurt","volume":"2021","author":"Nakkiran","year":"2021","journal-title":"J. Statis. Mech. Theory Experi."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TMRB.2021.3060032","article-title":"Method for automatic slippage detection with tactile sensors embedded in prosthetic hands","volume":"3","author":"Romeo","year":"2021","journal-title":"IEEE Trans. Med. Robo. Bionics"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"73027","DOI":"10.1109\/ACCESS.2020.2987849","article-title":"Methods and sensors for slip detection in robotics: a survey","volume":"8","author":"Romeo","year":"2020","journal-title":"IEEE Access"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","article-title":"The discrete wavelet transform: wedding the a trous and Mallat algorithms","volume":"40","author":"Shensa","year":"1992","journal-title":"IEEE Trans. Signal Process."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/TRO.2013.2279630","article-title":"Efficient break-away friction ratio and slip prediction based on haptic surface exploration","volume":"30","author":"Song","year":"2013","journal-title":"IEEE Trans. Robot."},{"key":"ref30","first-page":"6000","author":"Vaswani","year":""},{"key":"ref31","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/BF00238156","article-title":"Factors influencing the force control during precision grip","volume":"53","author":"Westling","year":"1984","journal-title":"Exp. Brain Res."},{"key":"ref32","first-page":"1","article-title":"Deep learning LSTM-based slip detection for robotic grasping","author":"Xie","year":"2023"},{"key":"ref33","first-page":"3537","article-title":"Detection of slip from vision and touch","author":"Yan","year":"2022"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"9600","DOI":"10.1109\/ACCESS.2021.3049854","article-title":"A multi-threshold-based force regulation policy for prosthetic hand preventing slippage","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Access"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"523","DOI":"10.3390\/s19030523","article-title":"Learning Spatio temporal tactile features with a ConvLSTM for the direction of slip detection","volume":"19","author":"Zapata-Impata","year":"2019","journal-title":"Sensors"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1108\/IR-07-2021-0133","article-title":"Design and slip prevention control of a multi-sensory anthropomorphic prosthetic hand","volume":"49","author":"Zeng","year":"2022","journal-title":"Indus. Robo. Int. J. Robo. Res. App."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"7073","DOI":"10.1109\/JSEN.2016.2596840","article-title":"Initial slip detection and its application in biomimetic robotic hands","volume":"16","author":"Zhang","year":"2016","journal-title":"IEEE Sensors J."},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.02653","article-title":"FingerVision tactile sensor design and slip detection using convolutional LSTM network","author":"Zhang","year":"2018","journal-title":"ArXiv"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1478758\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T13:14:49Z","timestamp":1738934089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2025.1478758\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,5]]},"references-count":38,"alternative-id":["10.3389\/fnbot.2025.1478758"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2025.1478758","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,5]]},"article-number":"1478758"}}