{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T04:32:34Z","timestamp":1782361954859,"version":"3.54.5"},"reference-count":20,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"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":[[2022,6,30]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>How to make accurate action decisions based on visual information is one of the important research directions of industrial robots. The purpose of this paper is to design a highly optimized hand-eye coordination model of the robot to improve the robots\u2019 on-site decision-making ability.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The combination of inverse reinforcement learning (IRL) algorithm and generative adversarial network can effectively reduce the dependence on expert samples and robots can obtain the decision-making performance that the degree of optimization is not lower than or even higher than that of expert samples.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The performance of the proposed model is verified in the simulation environment and real scene. By monitoring the reward distribution of the reward function and the trajectory of the robot, the proposed model is compared with other existing methods. The experimental results show that the proposed model has better decision-making performance in the case of less expert data.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>A robot hand-eye cooperation model based on improved IRL is proposed and verified. Empirical investigations on real experiments reveal that overall, the proposed approach tends to improve the real efficiency by more than 10% when compared to alternative hand-eye cooperation methods.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ir-09-2021-0208","type":"journal-article","created":{"date-parts":[[2021,11,29]],"date-time":"2021-11-29T03:36:55Z","timestamp":1638157015000},"page":"877-884","source":"Crossref","is-referenced-by-count":4,"title":["Robot hand-eye cooperation based on improved inverse reinforcement learning"],"prefix":"10.1108","volume":"49","author":[{"given":"Ning","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Nan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Ku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"issue":"4","key":"key2022062914055412100_ref001","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.3390\/s21041292","article-title":"Reinforcement learning approaches in social robotics","volume":"21","year":"2021","journal-title":"Sensors"},{"issue":"2","key":"key2022062914055412100_ref002","first-page":"257","article-title":"An infant development-inspired approach to robot hand-eye coordination","volume":"11","year":"2014","journal-title":"International Journal of Advanced Robotic Systems"},{"issue":"5","key":"key2022062914055412100_ref004","first-page":"24","article-title":"Review on deep inverse reinforcement learning","volume":"54","year":"2018","journal-title":"Computer Engineering and Applications"},{"issue":"22","key":"key2022062914055412100_ref003","first-page":"119","article-title":"Maximum entropy inverse reinforcement learning based on generative adversarial networks","volume":"55","year":"2019","journal-title":"Computer Engineering and Applications"},{"key":"key2022062914055412100_ref005","first-page":"2672","article-title":"Generative adversarial networks","volume":"3","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"12","key":"key2022062914055412100_ref006","first-page":"2859","article-title":"Developmental robotic 3D reaching based on double-network architecture","volume":"8","year":"2012","journal-title":"Computer Knowledge and Technology"},{"key":"key2022062914055412100_ref007","doi-asserted-by":"crossref","unstructured":"Kim, H., Kuc, T. and Lee, K. 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(2018), \u201cResearch on hand-eye coordination system of industrial robot based on Jacobian matrix\u201d, Working paper, 1 June, College of Mechanical and Electrical Engineering, Changchun University of Technology, Changchun."},{"issue":"3","key":"key2022062914055412100_ref015","first-page":"233","article-title":"Visual tracking of robotic calibration-free hand-eye coordination system","volume":"39","year":"2018","journal-title":"Journal of Changchun University of Technology"},{"key":"key2022062914055412100_ref016","volume-title":"Reinforcement Learning: An Introduction","year":"2018"},{"issue":"6","key":"key2022062914055412100_ref018","first-page":"641","article-title":"Smart manufacturing space","volume":"46","year":"2017","journal-title":"Information and Control"},{"key":"key2022062914055412100_ref017","article-title":"Generalization through hand-eye coordination: an action space for learning spatially-invariant visuomotor control","year":"2021"},{"key":"key2022062914055412100_ref019","unstructured":"You, F. 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