{"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/catgrasp-learning-category-level-task","title":"CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation","arxiv_id":"2109.09163","date":"2021-09-19","proceeding":null,"authors":["Bowen Wen","Wenzhao Lian","Kostas Bekris","Stefan Schaal"],"abstract":"Task-relevant grasping is critical for industrial assembly, where downstream manipulation tasks constrain the set of valid grasps. Learning how to perform this task, however, is challenging, since task-relevant grasp labels are hard to define and annotate. There is also yet no consensus on proper representations for modeling or off-the-shelf tools for performing task-relevant grasps. This work proposes a framework to learn task-relevant grasping for industrial objects without the need of time-consuming real-world data collection or manual annotation. To achieve this, the entire framework is trained solely in simulation, including supervised training with synthetic label generation and self-supervised, hand-object interaction. In the context of this framework, this paper proposes a novel, object-centric canonical representation at the category level, which allows establishing dense correspondence across object instances and transferring task-relevant grasps to novel instances. Extensive experiments on task-relevant grasping of densely-cluttered industrial objects are conducted in both simulation and real-world setups, demonstrating the effectiveness of the proposed framework. Code and data are available at https://sites.google.com/view/catgrasp.","url_abs":"https://arxiv.org/abs/2109.09163v2","url_pdf":"https://arxiv.org/pdf/2109.09163v2.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":"catgrasp-learning-category-level-task","repo_url":"https://github.com/wenbowen123/catgrasp","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"domain-generalization","task_name":"Domain Generalization"},{"task_slug":"human-grasp-contact-prediction","task_name":"Grasp Contact Prediction"},{"task_slug":"grasp-generation","task_name":"Grasp Generation"},{"task_slug":"industrial-robots","task_name":"Industrial Robots"},{"task_slug":"object","task_name":"Object"},{"task_slug":"physical-simulations","task_name":"Physical Simulations"},{"task_slug":"robot-task-planning","task_name":"Robot Task Planning"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.09163","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}