{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/robotic-grasp-detection-using-deep","title":"Robotic Grasp Detection using Deep Convolutional Neural Networks","arxiv_id":"1611.08036","date":"2016-11-24","proceeding":null,"authors":["Sulabh Kumra","Christopher Kanan"],"abstract":"Deep learning has significantly advanced computer vision and natural language\nprocessing. While there have been some successes in robotics using deep\nlearning, it has not been widely adopted. In this paper, we present a novel\nrobotic grasp detection system that predicts the best grasping pose of a\nparallel-plate robotic gripper for novel objects using the RGB-D image of the\nscene. The proposed model uses a deep convolutional neural network to extract\nfeatures from the scene and then uses a shallow convolutional neural network to\npredict the grasp configuration for the object of interest. Our multi-modal\nmodel achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and\nruns at real-time speeds. This redefines the state-of-the-art for robotic grasp\ndetection.","url_abs":"http://arxiv.org/abs/1611.08036v4","url_pdf":"http://arxiv.org/pdf/1611.08036v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robotic-grasping-on-cornell-grasp-dataset-1","task":"Robotic Grasping","dataset":"Cornell Grasp Dataset","model":"Multi-Modal Grasp Predictor","rank_in_archive_order":4,"of":7,"metrics":{"5 fold cross validation":"89.21"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.08036","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}