Ali Ayub, Zachary De Francesco, Jainish Mehta, Khaled Yaakoub Agha, Patrick Holthaus, C. Nehaniv, Kerstin Dautenhahn
{"title":"以人为本的持续学习观:了解交互、教学模式和人类用户对重复交互中持续学习机器人的看法","authors":"Ali Ayub, Zachary De Francesco, Jainish Mehta, Khaled Yaakoub Agha, Patrick Holthaus, C. Nehaniv, Kerstin Dautenhahn","doi":"10.1145/3659110","DOIUrl":null,"url":null,"abstract":"\n Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been\n robot-centered\n to develop continual learning algorithms that can quickly learn new information on systematically collected static datasets. In this paper, we take a\n human-centered\n approach to continual learning, to understand how humans interact with, teach, and perceive continual learning robots over the long term, and if there are variations in their teaching styles. We developed a socially guided continual learning system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the continual learning robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Finally, our analysis shows that although users have concerns about continual learning robots being deployed in our daily lives, they mention that with further improvements continual learning robots could assist older adults and people with disabilities in their homes.\n","PeriodicalId":36515,"journal":{"name":"ACM Transactions on Human-Robot Interaction","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Human-Centered View of Continual Learning: Understanding Interactions, Teaching Patterns, and Perceptions of Human Users Towards a Continual Learning Robot in Repeated Interactions\",\"authors\":\"Ali Ayub, Zachary De Francesco, Jainish Mehta, Khaled Yaakoub Agha, Patrick Holthaus, C. Nehaniv, Kerstin Dautenhahn\",\"doi\":\"10.1145/3659110\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been\\n robot-centered\\n to develop continual learning algorithms that can quickly learn new information on systematically collected static datasets. In this paper, we take a\\n human-centered\\n approach to continual learning, to understand how humans interact with, teach, and perceive continual learning robots over the long term, and if there are variations in their teaching styles. We developed a socially guided continual learning system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the continual learning robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Finally, our analysis shows that although users have concerns about continual learning robots being deployed in our daily lives, they mention that with further improvements continual learning robots could assist older adults and people with disabilities in their homes.\\n\",\"PeriodicalId\":36515,\"journal\":{\"name\":\"ACM Transactions on Human-Robot Interaction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Human-Robot Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3659110\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ROBOTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Human-Robot Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3659110","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
A Human-Centered View of Continual Learning: Understanding Interactions, Teaching Patterns, and Perceptions of Human Users Towards a Continual Learning Robot in Repeated Interactions
Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been
robot-centered
to develop continual learning algorithms that can quickly learn new information on systematically collected static datasets. In this paper, we take a
human-centered
approach to continual learning, to understand how humans interact with, teach, and perceive continual learning robots over the long term, and if there are variations in their teaching styles. We developed a socially guided continual learning system that integrates CL models for object recognition with a mobile manipulator robot and allows humans to directly teach and test the robot in real time over multiple sessions. We conducted an in-person study with 60 participants who interacted with the continual learning robot in 300 sessions with 5 sessions per participant. In this between-participant study, we used three different CL models deployed on a mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. Our analysis shows that the constrained experimental setups that have been widely used to test most CL models are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Finally, our analysis shows that although users have concerns about continual learning robots being deployed in our daily lives, they mention that with further improvements continual learning robots could assist older adults and people with disabilities in their homes.
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.