{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T17:17:28Z","timestamp":1784135848815,"version":"3.55.0"},"reference-count":64,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose a dual-module robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from the n-channel image of the scene. We present an improved version of the Generative Residual Convolutional Neural Network (GR-ConvNet v2) model that can generate robust antipodal grasps from n-channel image input at real-time speeds (20 ms). We evaluated the proposed model architecture on three standard datasets and achieved a new state-of-the-art accuracy of 98.8%, 95.1%, and 97.4% on Cornell, Jacquard and Graspnet grasping datasets, respectively. Empirical results show that our model significantly outperformed the prior work with a stricter IoU-based grasp detection metric. We conducted a suite of tests in simulation and the real world on a diverse set of previously unseen objects with adversarial geometry and household items. We demonstrate the adaptability of our approach by directly transferring the trained model to a 7 DoF robotic manipulator with a grasp success rate of 95.4% and 93.0% on novel household and adversarial objects, respectively. Furthermore, we validate the generalization capability of our pixel-wise grasp prediction model by validating it on complex Ravens-10 benchmark tasks, some of which require closed-loop visual feedback for multi-step sequencing.<\/jats:p>","DOI":"10.3390\/s22166208","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"6208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["GR-ConvNet v2: A Real-Time Multi-Grasp Detection Network for Robotic Grasping"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2792-0969","authenticated-orcid":false,"given":"Sulabh","family":"Kumra","sequence":"first","affiliation":[{"name":"The Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"},{"name":"eBots Inc., Fremont, CA 94539, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirin","family":"Joshi","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"},{"name":"Siemens Corporation, Berkeley, CA 94704, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9813-7165","authenticated-orcid":false,"given":"Ferat","family":"Sahin","sequence":"additional","affiliation":[{"name":"The Department of Electrical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1177\/0278364914549607","article-title":"Deep learning for detecting robotic grasps","volume":"34","author":"Lenz","year":"2015","journal-title":"Int. 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