{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:42:41Z","timestamp":1765356161216,"version":"3.41.2"},"reference-count":35,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2017,1,16]],"date-time":"2017-01-16T00:00:00Z","timestamp":1484524800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IR"],"published-print":{"date-parts":[[2017,1,16]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>Development of autonomous robot manipulator for human-robot assembly tasks is a key component to reach high effectiveness. In such tasks, the robot real-time object recognition is crucial. In addition, the need for simple and safe teaching techniques need to be considered, because: small size robot manipulators\u2019 presence in everyday life environments is increasing requiring non-expert operators to teach the robot; and in small size applications, the operator has to teach several different motions in a short time.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>For object recognition, the authors propose a deep belief neural network (DBNN)-based approach. The captured camera image is used as the input of the DBNN. The DBNN extracts the object features in the intermediate layers. In addition, the authors developed three teaching systems which utilize iPhone; haptic; and Kinect devices.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The object recognition by DBNN is robust for real-time applications. The robot picks up the object required by the user and places it in the target location. Three developed teaching systems are easy to use by non-experienced subjects, and they show different performance in terms of time to complete the task and accuracy.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title>\n<jats:p>The proposed method can ease the use of robot manipulators helping non-experienced users completing different assembly tasks.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This work applies DBNN for object recognition and three intuitive systems for teaching robot manipulators.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-05-2016-0140","type":"journal-article","created":{"date-parts":[[2017,1,26]],"date-time":"2017-01-26T06:50:19Z","timestamp":1485413419000},"page":"11-20","source":"Crossref","is-referenced-by-count":30,"title":["Pick-place of dynamic objects by robot manipulator based on deep learning and easy user interface teaching systems"],"prefix":"10.1108","volume":"44","author":[{"given":"Delowar","family":"Hossain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genci","family":"Capi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitsuru","family":"Jindai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shin-ichiro","family":"Kaneko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"first-page":"1875","article-title":"Teachingless spray-painting of sculptured surface by an industrial robot","year":"1997","key":"key2020121021324837400_ref001"},{"issue":"4","key":"key2020121021324837400_ref002","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1287\/opre.1050.0210","article-title":"Erratum to \u2018the three-dimensional bin packing problem\u2019: robot-packable and orthogonal variants of packing problems","volume":"53","year":"2005","journal-title":"Operations Research"},{"issue":"3","key":"key2020121021324837400_ref003","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/027836498600500301","article-title":"3DPO: a three-dimensional part orientation system","volume":"5","year":"1986","journal-title":"International Journal of Robotics Research"},{"issue":"1","key":"key2020121021324837400_ref004","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.arcontrol.2007.01.002","article-title":"Present and future robot control development: an industrial perspective","volume":"31","year":"2007","journal-title":"Annual Reviews in Control"},{"issue":"3","key":"key2020121021324837400_ref005","first-page":"395","article-title":"Quantitative evaluation of an intuitive teaching method for industrial robot using a force\/moment direction sensor","volume":"1","year":"2003","journal-title":"International Journal of Control, Automation, and Systems"},{"issue":"4","key":"key2020121021324837400_ref006","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1108\/01439910310479612","article-title":"Robotic manipulation of food products \u2013 a review","volume":"30","year":"2003","journal-title":"Industrial Robot: An International Journal"},{"issue":"2","key":"key2020121021324837400_ref007","first-page":"130","article-title":"From volumes to views: an approach to 3-D object recognition","volume":"55","year":"1992","journal-title":"Computer Vision, Graphics, and Image Processing: Image Understanding"},{"issue":"3","key":"key2020121021324837400_ref008","first-page":"283","article-title":"Two-wheeled welding mobile robot for tracking a smooth curved welding path using adaptive sliding-mode control technique","volume":"5","year":"2007","journal-title":"International Journal of Control, Automation, and Systems"},{"issue":"3","key":"key2020121021324837400_ref009","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1177\/027836498600500302","article-title":"The representation, recognition, and locating of 3-D","volume":"5","year":"1986","journal-title":"International Journal of Robotics Research"},{"year":"2012","key":"key2020121021324837400_ref010","article-title":"Semantic place recognition based on deep belief networks and tiny images"},{"issue":"7","key":"key2020121021324837400_ref011","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","year":"2006","journal-title":"Neural Computation"},{"issue":"9","key":"key2020121021324837400_ref012","first-page":"82","article-title":"Deep neural networks for acoustic modeling in speech recognition","volume":"29","year":"2012","journal-title":"IEEE Signal Processing Magazine"},{"issue":"3","key":"key2020121021324837400_ref013","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/09511920903529255","article-title":"Metrology-assisted robotic processing of aerospace applications","volume":"23","year":"2010","journal-title":"International Journal of Computer Integrated Manufacturing"},{"year":"2015","key":"key2020121021324837400_ref014","article-title":"A brief survey on deep belief networks and introducing a new object oriented MATLAB toolbox (DeeBNet V2.0)"},{"issue":"3","key":"key2020121021324837400_ref015","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/34.75511","article-title":"3-D object recognition using bipartite matching embedded in discrete relaxation","volume":"13","year":"1991","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"key2020121021324837400_ref016","first-page":"105","article-title":"Part-level object recognition using superquadrics","volume":"5","year":"2004","journal-title":"Computer Vision and Image Understanding"},{"key":"key2020121021324837400_ref017","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/978-3-319-04117-9_12","article-title":"3D Object classification using deep belief networks","volume":"8326","year":"2014","journal-title":"Multi Media Modeling"},{"issue":"4-5","key":"key2020121021324837400_ref018","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1177\/0278364914549607","article-title":"Deep learning for detecting robotic grasps","volume":"34","year":"2015","journal-title":"International Journal of Robotics Research"},{"year":"2016","key":"key2020121021324837400_ref019","article-title":"Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection"},{"first-page":"401","article-title":"A 3D object recognition and pose estimation system using deep learning method","year":"2014","key":"key2020121021324837400_ref020"},{"first-page":"1","article-title":"Deep belief networks for phone recognition","year":"2009","key":"key2020121021324837400_ref022"},{"issue":"2","key":"key2020121021324837400_ref021","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1108\/IR-10-2013-409","article-title":"Adaptive arm motion generation of humanoid robot operating in dynamic environments","volume":"41","year":"2014","journal-title":"Industrial Robot: An International Journal"},{"key":"key2020121021324837400_ref023","first-page":"1339","article-title":"3D Object recognition with deep belief nets","volume":"22","year":"2009","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"key2020121021324837400_ref024","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/0004-3702(77)90006-6","article-title":"Description and recognition of curved objects","volume":"8","year":"1977","journal-title":"Artificial Intelligence"},{"first-page":"1","article-title":"Shape-primitive based object recognition and grasping","year":"2012","key":"key2020121021324837400_ref025"},{"first-page":"409","article-title":"Teachless teach-repeat: toward vision-based programming of industrial robots","year":"2012","key":"key2020121021324837400_ref026"},{"year":"2015","key":"key2020121021324837400_ref027","article-title":"Supersizing Self-supervision: learning to grasp from 50K Tries and 700 Robot Hours"},{"first-page":"1316","article-title":"Real-time grasp detection using convolutional neural networks","year":"2015","key":"key2020121021324837400_ref028"},{"first-page":"370","article-title":"Sharpening haptic inputs for teaching a manipulation skill to a robot","year":"2010","key":"key2020121021324837400_ref029"},{"year":"2006","key":"key2020121021324837400_ref030","article-title":"Teaching by demonstration of robotic manipulators in non-stationary environments"},{"first-page":"1526","article-title":"A novel inference of a restricted Boltzmann machine","year":"2014","key":"key2020121021324837400_ref031"},{"key":"key2020121021324837400_ref032","first-page":"1","article-title":"Robotic grasping recognition using multi-modal deep extreme learning machine","year":"2016","journal-title":"Multidimensional Systems and Signal Processing"},{"issue":"5","key":"key2020121021324837400_ref033","article-title":"Two-dimensional irregular parts packing with genetic algorithm","volume":"14","year":"2002","journal-title":"Journal of Computer Aided Design & Computer Graphics"},{"first-page":"1175","article-title":"A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation","year":"2013","key":"key2020121021324837400_ref034"},{"year":"2015","key":"key2020121021324837400_ref035","article-title":"Towards vision-based deep reinforcement learning for robotic motion control"}],"container-title":["Industrial Robot: An International Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IR-05-2016-0140\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IR-05-2016-0140\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T21:39:03Z","timestamp":1753393143000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ir\/article\/44\/1\/11-20\/182964"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,16]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1,16]]}},"alternative-id":["10.1108\/IR-05-2016-0140"],"URL":"https:\/\/doi.org\/10.1108\/ir-05-2016-0140","relation":{},"ISSN":["0143-991X"],"issn-type":[{"type":"print","value":"0143-991X"}],"subject":[],"published":{"date-parts":[[2017,1,16]]}}}