{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:54:40Z","timestamp":1777715680672,"version":"3.51.4"},"reference-count":38,"publisher":"SAGE Publications","issue":"10-11","license":[{"start":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T00:00:00Z","timestamp":1566172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Australian Research Council's Linkage Infrastructure, Equipment and Facilities scheme","award":["LE160100090"],"award-info":[{"award-number":["LE160100090"]}]},{"DOI":"10.13039\/501100022594","name":"Australian Research Council Centre of Excellence for Robotic Vision","doi-asserted-by":"crossref","award":["CE140100016"],"award-info":[{"award-number":["CE140100016"]}],"id":[{"id":"10.13039\/501100022594","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2019,9]]},"abstract":"<jats:p>Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labeling process is often expensive or even impractical in many robotic applications. In this article, we introduce an adversarial discriminative sim-to-real transfer approach to reduce the amount of labeled real data required. The effectiveness of the approach is demonstrated with modular networks in a table-top object-reaching task where a seven-degree-of-freedom arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations from a monocular RGB camera. The adversarial transfer approach reduced the labeled real data requirement by 50%. Policies can be transferred to real environments with only 93 labeled and 186 unlabeled real images. The transferred visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 97.8% success rate and 1.8 cm control accuracy. Datasets and code are openly available.<\/jats:p>","DOI":"10.1177\/0278364919870227","type":"journal-article","created":{"date-parts":[[2019,8,20]],"date-time":"2019-08-20T02:05:06Z","timestamp":1566266706000},"page":"1229-1245","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":33,"title":["Adversarial discriminative sim-to-real transfer of visuo-motor policies"],"prefix":"10.1177","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3938-5377","authenticated-orcid":false,"given":"Fangyi","family":"Zhang","sequence":"first","affiliation":[{"name":"Australian Centre for Robotic Vision (ACRV), Brisbane, QLD, Australia"},{"name":"Queensland University of Technology (QUT), Brisbane, QLD, Australia"},{"name":"Alibaba Group, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00fcrgen","family":"Leitner","sequence":"additional","affiliation":[{"name":"Australian Centre 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