{"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/jacquard-a-large-scale-dataset-for-robotic","title":"Jacquard: A Large Scale Dataset for Robotic Grasp Detection","arxiv_id":"1803.11469","date":"2018-03-30","proceeding":null,"authors":["Amaury Depierre","Emmanuel Dellandréa","Liming Chen"],"abstract":"Grasping skill is a major ability that a wide number of real-life\napplications require for robotisation. State-of-the-art robotic grasping\nmethods perform prediction of object grasp locations based on deep neural\nnetworks. However, such networks require huge amount of labeled data for\ntraining making this approach often impracticable in robotics. In this paper,\nwe propose a method to generate a large scale synthetic dataset with ground\ntruth, which we refer to as the Jacquard grasping dataset. Jacquard is built on\na subset of ShapeNet, a large CAD models dataset, and contains both RGB-D\nimages and annotations of successful grasping positions based on grasp attempts\nperformed in a simulated environment. We carried out experiments using an\noff-the-shelf CNN, with three different evaluation metrics, including real\ngrasping robot trials. The results show that Jacquard enables much better\ngeneralization skills than a human labeled dataset thanks to its diversity of\nobjects and grasping positions. For the purpose of reproducible research in\nrobotics, we are releasing along with the Jacquard dataset a web interface for\nresearchers to evaluate the successfulness of their grasping position\ndetections using our dataset.","url_abs":"http://arxiv.org/abs/1803.11469v2","url_pdf":"http://arxiv.org/pdf/1803.11469v2.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":"jacquard-a-large-scale-dataset-for-robotic","repo_url":"https://github.com/TianheWu/LGPNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11469","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}