{"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-pick-and-place-of-novel-objects-in","title":"Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching","arxiv_id":"1710.01330","date":"2017-10-03","proceeding":null,"authors":["Andy Zeng","Shuran Song","Kuan-Ting Yu","Elliott Donlon","Francois R. Hogan","Maria Bauza","Daolin Ma","Orion Taylor","Melody Liu","Eudald Romo","Nima Fazeli","Ferran Alet","Nikhil Chavan Dafle","Rachel Holladay","Isabella Morona","Prem Qu Nair","Druck Green","Ian Taylor","Weber Liu","Thomas Funkhouser","Alberto Rodriguez"],"abstract":"This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu","url_abs":"https://arxiv.org/abs/1710.01330v5","url_pdf":"https://arxiv.org/pdf/1710.01330v5.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":"robotic-pick-and-place-of-novel-objects-in","repo_url":"https://github.com/andyzeng/arc-robot-vision","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"robotic-pick-and-place-of-novel-objects-in","repo_url":"https://github.com/hz-ants/arc-robot-vision","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"robotic-pick-and-place-of-novel-objects-in","repo_url":"https://github.com/kmi-robots/object_reasoner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"robotic-grasping","task_name":"Robotic Grasping"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1710.01330","atlas_url":"https://app.syntology.ai/?focus=1710.01330","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}