{"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/steering-a-predator-robot-using-a-mixed","title":"Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network","arxiv_id":"1606.09433","date":"2016-06-30","proceeding":null,"authors":["Diederik Paul Moeys","Federico Corradi","Emmett Kerr","Philip Vance","Gautham Das","Daniel Neil","Dermot Kerr","Tobi Delbruck"],"abstract":"This paper describes the application of a Convolutional Neural Network (CNN)\nin the context of a predator/prey scenario. The CNN is trained and run on data\nfrom a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot\n(the predator), which follows another one (the prey). The CNN is driven by both\nconventional image frames and dynamic vision sensor \"frames\" that consist of a\nconstant number of DAVIS ON and OFF events. The network is thus \"data driven\"\nat a sample rate proportional to the scene activity, so the effective sample\nrate varies from 15 Hz to 240 Hz depending on the robot speeds. The network\ngenerates four outputs: steer right, left, center and non-visible. After\noff-line training on labeled data, the network is imported on the on-board\nSummit XL robot which runs jAER and receives steering directions in real time.\nSuccessful results on closed-loop trials, with accuracies up to 87% or 92%\n(depending on evaluation criteria) are reported. Although the proposed approach\ndiscards the precise DAVIS event timing, it offers the significant advantage of\ncompatibility with conventional deep learning technology without giving up the\nadvantage of data-driven computing.","url_abs":"http://arxiv.org/abs/1606.09433v1","url_pdf":"http://arxiv.org/pdf/1606.09433v1.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":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"pred18","name":"PRED18","full_name":"PRED18: Predator/Prey DAVIS Dataset"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.09433","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}