{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T11:42:34Z","timestamp":1730202154266,"version":"3.28.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,8,1]],"date-time":"2020-08-01T00:00:00Z","timestamp":1596240000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1109\/case48305.2020.9216788","type":"proceedings-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T20:01:51Z","timestamp":1602187311000},"page":"1243-1248","source":"Crossref","is-referenced-by-count":3,"title":["Learning to Predict Friction and Classify Contact States by Tactile Sensor"],"prefix":"10.1109","author":[{"given":"Xingru","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Zheng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaojun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Houde","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Liang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","first-page":"802","article-title":"Convolutionallstm network: A machine learning approach for precipitation nowcasting","author":"xingjian","year":"2015","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref11","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"2016","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref12","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7354090"},{"key":"ref14","first-page":"1","article-title":"Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors","author":"zhang","year":"2019","journal-title":"no CoRL"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/HUMANOIDS.2015.7363558"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460495"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139016"},{"key":"ref18","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"ArXiv"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01086"},{"key":"ref4","article-title":"The feeling of success: Does touch sensing help predict grasp outcomes?","author":"calandra","year":"2017","journal-title":"arXiv preprint arXiv 1710 05512"},{"key":"ref3","first-page":"4276","author":"lee","year":"2019","journal-title":"‘Touching to See’ and ‘Seeing to Feel’ Robotic Cross-modal Sensory Data Generation for VisualTactile Perception"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2969179"},{"key":"ref5","article-title":"Dense tactile force distribution estimation using gelslim and inverse fem","author":"ma","year":"2018","journal-title":"arXiv preprint arXiv 1810 06008"},{"key":"ref8","article-title":"Maintaining grasps within slipping bound by monitoring incipient slip","author":"dong","year":"2018","journal-title":"arXiv preprint arXiv 1810 10053"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2018.8593430"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012759440-8\/50025-6"},{"journal-title":"Object grasping planning for the situation when soft and rigid objects are mixed together","year":"2019","author":"wang","key":"ref1"},{"key":"ref9","first-page":"64","article-title":"Unsupervised learning for physical interaction through video prediction","author":"finn","year":"2016","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"ref20","first-page":"613","article-title":"Generating videos with scene dynamics","author":"vondrick","year":"2016","journal-title":"Adv Neural Inf Process Syst NIPS"}],"event":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","start":{"date-parts":[[2020,8,20]]},"location":"Hong Kong, Hong Kong","end":{"date-parts":[[2020,8,21]]}},"container-title":["2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9210430\/9216730\/09216788.pdf?arnumber=9216788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:54:23Z","timestamp":1656345263000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9216788\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/case48305.2020.9216788","relation":{},"subject":[],"published":{"date-parts":[[2020,8]]}}}