{"id":409,"date":"2019-10-21T11:16:02","date_gmt":"2019-10-21T02:16:02","guid":{"rendered":"http:\/\/irobot.dgu.edu\/?p=409"},"modified":"2019-10-21T11:16:02","modified_gmt":"2019-10-21T02:16:02","slug":"journal-paper-%ea%b2%8c%ec%9e%acieee-access-sequential-image-based-attention-network-for-inferring-force-estimation-without-haptic-sensor","status":"publish","type":"post","link":"http:\/\/irobot.dgu.edu\/wordpress\/journal-paper-%ea%b2%8c%ec%9e%acieee-access-sequential-image-based-attention-network-for-inferring-force-estimation-without-haptic-sensor\/","title":{"rendered":"Journal Paper \uac8c\uc7ac(IEEE ACCESS) : Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor"},"content":{"rendered":"<p><strong>Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor<\/strong><br \/>\nHochul Shin, Hyeon Cho, Dongyi Kim, Dae-kwan Ko, <strong>Soo-Chul Lim<\/strong>*\u00a0 and\u00a0 Wonjun Hwang*<\/p>\n<p>&nbsp;<\/p>\n<p>Humans can approximately infer the force of interaction between objects using only visual information because we have learned it through experiences. Based on this idea, in this paper, we propose a method based on a recurrent convolutional neural network that uses sequential images to infer the interaction force without using a haptic sensor. To train and validate deep learning methods, we collected a large number of images and corresponding data concerning the interaction forces between objects shown therein through an electronic motor-based device. To focus on the changing appearances of a target object owing to external force in the images, we develop a sequential image-based attention module that learns a salient model from temporal dynamics for predicting unknown interaction forces. We propose a sequential image-based spatial attention module and a sequential image-based channel attention module, which are extended to exploit multiple images based on corresponding weighted average pooling layers. Extensive experimental results verified that the proposed method can successfully infer interaction forces in various conditions featuring different target materials, changes in illumination, and directions of external forces.<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Date of Publication:<\/strong> 11 October 2019 <i class=\"doc-abs-pubdate-help\"><\/i><\/p>\n<div id=\"LayoutWrapper\">\n<div class=\"container-fluid\">\n<div class=\"row\">\n<div class=\"col\">\n<div class=\"ng-scope\">\n<div class=\"global-content-wrapper\">\n<div class=\"row document ng-document stats-document\">\n<div class=\"document-main col-9\">\n<div class=\"row document-main-body\">\n<div class=\"document-main-content-container\">\n<section class=\"tab-pane col-24-24 u-printing-display-inline-ie u-printing-display-inline-ff\">\n<div class=\"document-main-left-trail-content\">\n<section class=\"document-abstract document-tab\">\n<div class=\"abstract-desktop-div hide-mobile\">\n<div class=\"row u-pt-1\">\n<div class=\"col-6\">\n<div class=\"u-pb-1\">\n<div>\n<div><strong>Electronic ISSN:<\/strong> 2169-3536<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div class=\"col-6\">\n<div class=\"u-pb-1 stats-document-abstract-doi\"><strong>DOI: <\/strong> <a class=\"ng-isolate-scope\" href=\"https:\/\/doi.org\/10.1109\/ACCESS.2019.2947090\" target=\"_blank\">10.1109\/ACCESS.2019.2947090<\/a><\/div>\n<div class=\"u-pb-1 doc-abstract-publisher publisher-info-container black-tooltip\"><strong>Publisher:<\/strong> <span class=\"publisher-info-label\">IEEE<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<\/div>\n<\/section>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor Hochul Shin, Hyeon Cho, Dongyi Kim, Dae-kwan Ko, Soo-Chul Lim*\u00a0 and\u00a0 Wonjun Hwang* &nbsp; Humans can approximately infer the force of interaction between objects using only visual information because we have learned it through experiences. Based on this idea, in this paper, we propose&hellip;&nbsp;<a href=\"http:\/\/irobot.dgu.edu\/wordpress\/journal-paper-%ea%b2%8c%ec%9e%acieee-access-sequential-image-based-attention-network-for-inferring-force-estimation-without-haptic-sensor\/\" class=\"\" rel=\"bookmark\">\ub354 \ubcf4\uae30 &raquo;<span class=\"screen-reader-text\">Journal Paper \uac8c\uc7ac(IEEE ACCESS) : Sequential Image-based Attention Network for Inferring Force Estimation without Haptic Sensor<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/posts\/409"}],"collection":[{"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/comments?post=409"}],"version-history":[{"count":0,"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/posts\/409\/revisions"}],"wp:attachment":[{"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/media?parent=409"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/categories?post=409"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/irobot.dgu.edu\/wordpress\/wp-json\/wp\/v2\/tags?post=409"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}