{"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/fingervision-tactile-sensor-design-and-slip","title":"FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network","arxiv_id":"1810.02653","date":"2018-10-05","proceeding":null,"authors":["Yazhan Zhang","Zicheng Kan","Yu Alexander Tse","Yang Yang","Michael Yu Wang"],"abstract":"Tactile sensing is essential to the human perception system, so as to robot.\nIn this paper, we develop a novel optical-based tactile sensor \"FingerVision\"\nwith effective signal processing algorithms. This sensor is composed of soft\nskin with embedded marker array bonded to rigid frame, and a web camera with a\nfisheye lens. While being excited with contact force, the camera tracks the\nmovements of markers and deformation field is obtained. Compared to existing\ntactile sensors, our sensor features compact footprint, high resolution, and\nease of fabrication. Besides, utilizing the deformation field estimation, we\npropose a slip classification framework based on convolution Long Short Term\nMemory (convolutional LSTM) networks. The data collection process takes\nadvantage of the human sense of slip, during which human hand holds 12 daily\nobjects, interacts with sensor skin and labels data with a slip or non-slip\nidentity based on human feeling of slip. Our slip classification framework\nperforms high accuracy of 97.62% on the test dataset. It is expected to be\ncapable of enhancing the stability of robot grasping significantly, leading to\nbetter contact force control, finer object interaction and more active sensing\nmanipulation.","url_abs":"http://arxiv.org/abs/1810.02653v1","url_pdf":"http://arxiv.org/pdf/1810.02653v1.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":"fingervision-tactile-sensor-design-and-slip","repo_url":"https://github.com/adamzhang129/fv_sensor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}