Francesco De Lellis;Marco Coraggio;Nathan C. Foster;Riccardo Villa;Cristina Becchio;Mario Di Bernardo
{"title":"Data-Driven Architecture to Encode Information in the Kinematics of Robots and Artificial Avatars","authors":"Francesco De Lellis;Marco Coraggio;Nathan C. Foster;Riccardo Villa;Cristina Becchio;Mario Di Bernardo","doi":"10.1109/LCSYS.2024.3416071","DOIUrl":null,"url":null,"abstract":"We present a data-driven control architecture designed to encode specific information, such as the presence or absence of an emotion, in the movements of an avatar or robot driven by a human operator. Our strategy leverages a set of human-recorded examples as the core for generating information-rich kinematic signals. To ensure successful object grasping, we propose a deep reinforcement learning strategy. We validate our approach using an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"1919-1924"},"PeriodicalIF":2.4000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10559995","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Control Systems Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10559995/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
We present a data-driven control architecture designed to encode specific information, such as the presence or absence of an emotion, in the movements of an avatar or robot driven by a human operator. Our strategy leverages a set of human-recorded examples as the core for generating information-rich kinematic signals. To ensure successful object grasping, we propose a deep reinforcement learning strategy. We validate our approach using an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task.