{"title":"On Using Social Signals to Enable Flexible Error-Aware HRI","authors":"Maia Stiber, R. Taylor, Chien-Ming Huang","doi":"10.1145/3568162.3576990","DOIUrl":null,"url":null,"abstract":"Prior error management techniques often do not possess the versatility to appropriately address robot errors across tasks and scenarios. Their fundamental framework involves explicit, manual error management and implicit domain-specific information driven error management, tailoring their response for specific interaction contexts. We present a framework for approaching error-aware systems by adding implicit social signals as another information channel to create more flexibility in application. To support this notion, we introduce a novel dataset (composed of three data collections) with a focus on understanding natural facial action unit (AU) responses to robot errors during physical-based human-robot interactions---varying across task, error, people, and scenario. Analysis of the dataset reveals that, through the lens of error detection, using AUs as input into error management affords flexibility to the system and has the potential to improve error detection response rate. In addition, we provide an example real-time interactive robot error management system using the error-aware framework.","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Human-Robot Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3568162.3576990","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 5
Abstract
Prior error management techniques often do not possess the versatility to appropriately address robot errors across tasks and scenarios. Their fundamental framework involves explicit, manual error management and implicit domain-specific information driven error management, tailoring their response for specific interaction contexts. We present a framework for approaching error-aware systems by adding implicit social signals as another information channel to create more flexibility in application. To support this notion, we introduce a novel dataset (composed of three data collections) with a focus on understanding natural facial action unit (AU) responses to robot errors during physical-based human-robot interactions---varying across task, error, people, and scenario. Analysis of the dataset reveals that, through the lens of error detection, using AUs as input into error management affords flexibility to the system and has the potential to improve error detection response rate. In addition, we provide an example real-time interactive robot error management system using the error-aware framework.
期刊介绍:
ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain.
THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.