{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:26:22Z","timestamp":1775744782458,"version":"3.50.1"},"reference-count":67,"publisher":"SAGE Publications","issue":"4-5","license":[{"start":{"date-parts":[[2015,3,16]],"date-time":"2015-03-16T00:00:00Z","timestamp":1426464000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2015,4]]},"abstract":"<jats:p> We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects. In this work, we apply a deep learning approach to solve this problem, which avoids time-consuming hand-design of features. This presents two main challenges. First, we need to evaluate a huge number of candidate grasps. In order to make detection fast and robust, we present a two-step cascaded system with two deep networks, where the top detections from the first are re-evaluated by the second. The first network has fewer features, is faster to run, and can effectively prune out unlikely candidate grasps. The second, with more features, is slower but has to run only on the top few detections. Second, we need to handle multimodal inputs effectively, for which we present a method that applies structured regularization on the weights based on multimodal group regularization. We show that our method improves performance on an RGBD robotic grasping dataset, and can be used to successfully execute grasps on two different robotic platforms. <\/jats:p>","DOI":"10.1177\/0278364914549607","type":"journal-article","created":{"date-parts":[[2015,3,17]],"date-time":"2015-03-17T01:57:33Z","timestamp":1426557453000},"page":"705-724","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1273,"title":["Deep learning for detecting robotic grasps"],"prefix":"10.1177","volume":"34","author":[{"given":"Ian","family":"Lenz","sequence":"first","affiliation":[{"name":"Department of Computer Science, Cornell University, Ithaca, NY, USA"}]},{"given":"Honglak","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of EECS, University of Michigan, Ann Arbor, MI, USA"}]},{"given":"Ashutosh","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Cornell University, Ithaca, NY, USA"}]}],"member":"179","published-online":{"date-parts":[[2015,3,16]]},"reference":[{"issue":"1","key":"bibr1-0278364914549607","first-page":"1","volume":"2","author":"Bengio Y","year":"2009","journal-title":"FTML"},{"key":"bibr2-0278364914549607","author":"Bicchi A","year":"2000","journal-title":"ICRA"},{"key":"bibr3-0278364914549607","author":"Bo L","year":"2012","journal-title":"ISER"},{"key":"bibr4-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2013.2289018"},{"key":"bibr5-0278364914549607","author":"Bollini M","year":"2011","journal-title":"IROS PR2 Workshop"},{"key":"bibr6-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2003.812689"},{"key":"bibr7-0278364914549607","author":"Cadena C","year":"2013","journal-title":"ICRA workshop on semantic perception, mapping and exploration"},{"key":"bibr8-0278364914549607","author":"Coates A","year":"2011","journal-title":"ICDAR"},{"key":"bibr9-0278364914549607","author":"Coates A","year":"2011","journal-title":"NIPS"},{"key":"bibr10-0278364914549607","author":"Collet Romea A","year":"2009","journal-title":"ICRA"},{"key":"bibr11-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1177\/0278364911401765"},{"key":"bibr12-0278364914549607","first-page":"2493","volume":"12","author":"Collobert R","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"bibr13-0278364914549607","author":"Detry R","year":"2009","journal-title":"ICDL"},{"key":"bibr14-0278364914549607","author":"Dogar M","year":"2012","journal-title":"RSS"},{"key":"bibr15-0278364914549607","author":"Ekvall S","year":"2007","journal-title":"ICRA"},{"key":"bibr16-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2013.2279412"},{"key":"bibr17-0278364914549607","author":"Ferrari C","year":"1992","journal-title":"ICRA"},{"key":"bibr18-0278364914549607","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2011.VII.012"},{"key":"bibr19-0278364914549607","author":"Girshick R","year":"2014","journal-title":"CVPR"},{"key":"bibr20-0278364914549607","author":"Goldfeder C","year":"2009","journal-title":"ICRA"},{"key":"bibr21-0278364914549607","author":"Goodfellow I","year":"2009","journal-title":"NIPS"},{"key":"bibr22-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"bibr23-0278364914549607","author":"Huebner K","year":"2008","journal-title":"IROS"},{"key":"bibr24-0278364914549607","doi-asserted-by":"publisher","DOI":"10.1162\/089976601750264992"},{"key":"bibr25-0278364914549607","doi-asserted-by":"crossref","unstructured":"Hyv\u00e4rinen A, Karhunen J, Oja E (2002) Principal Component Analysis and Whitening, chapter 6. 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