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Using the DNN, the optimum height and width were set for the robot hand to grasp objects, whose geometric and mass centre points were also considered in obtaining the optimum grasping positions for the robot fingers and the object. The proposed algorithm was tested on 10 differently shaped objects and showed improved grip performance compared to conventional methods.<\/jats:p>","DOI":"10.1017\/s0263574720000703","type":"journal-article","created":{"date-parts":[[2020,7,20]],"date-time":"2020-07-20T10:19:32Z","timestamp":1595240372000},"page":"735-748","source":"Crossref","is-referenced-by-count":10,"title":["Stable Robotic Grasping of Multiple Objects using Deep Neural Networks"],"prefix":"10.1017","volume":"39","author":[{"given":"Dongeon","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ailing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4290-8087","authenticated-orcid":false,"given":"Jangmyung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2020,7,20]]},"reference":[{"key":"S0263574720000703_ref5","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574714002148"},{"key":"S0263574720000703_ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11633-006-0263-x"},{"key":"S0263574720000703_ref3","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/LRA.2019.2896485","article-title":"Learning from humans how to grasp: A data-driven architecture for autonomous grasping with anthropomorphic soft hands","volume":"4","author":"Calandra","year":"2019","journal-title":"IEEE Robot. 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