{"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/antipodal-robotic-grasping-using-generative","title":"Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network","arxiv_id":"1909.04810","date":"2019-09-11","proceeding":null,"authors":["Sulabh Kumra","Shirin Joshi","Ferat Sahin"],"abstract":"In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from n-channel image of the scene. We propose a novel Generative Residual Convolutional Neural Network (GR-ConvNet) model that can generate robust antipodal grasps from n-channel input at real-time speeds (~20ms). We evaluate the proposed model architecture on standard datasets and a diverse set of household objects. We achieved state-of-the-art accuracy of 97.7% and 94.6% on Cornell and Jacquard grasping datasets respectively. We also demonstrate a grasp success rate of 95.4% and 93% on household and adversarial objects respectively using a 7 DoF robotic arm.","url_abs":"https://arxiv.org/abs/1909.04810v4","url_pdf":"https://arxiv.org/pdf/1909.04810v4.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":[{"paper_slug":"antipodal-robotic-grasping-using-generative","repo_url":"https://github.com/skumra/baxter-pnp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"antipodal-robotic-grasping-using-generative","repo_url":"https://github.com/skumra/robotic-grasping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"antipodal-robotic-grasping-using-generative","repo_url":"https://github.com/SteveHao74/shahao_GR-ConvNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"antipodal-robotic-grasping-using-generative","repo_url":"https://github.com/qingchenkanlu/new_grasp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"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":"GR-ConvNet","rank_in_archive_order":2,"of":7,"metrics":{"5 fold cross validation":"97.7"},"uses_additional_data":false},{"leaderboard":"/sota/robotic-grasping-on-jacquard-dataset","task":"Robotic Grasping","dataset":"Jacquard dataset","model":"GR-ConvNet","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy (%)":"94.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1909.04810","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}