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

IF 4.2 Q2 ROBOTICS
Ali Ayub, Zachary De Francesco, Jainish Mehta, Khaled Yaakoub Agha, Patrick Holthaus, C. Nehaniv, Kerstin Dautenhahn
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引用次数: 0

摘要

近年来,持续学习(CL)已成为机器学习(ML)和人机交互(HRI)交叉领域的一个重要研究方向,目的是让机器人在与人类的长期互动中不断学习所处的环境。然而,大多数持续学习研究都是以机器人为中心,开发能够在系统收集的静态数据集上快速学习新信息的持续学习算法。在本文中,我们采用了以人为中心的持续学习方法,以了解人类如何与持续学习机器人进行长期互动、教学和感知,以及他们的教学风格是否存在差异。我们开发了一个社交引导的持续学习系统,该系统将用于物体识别的CL模型与移动操纵机器人整合在一起,并允许人类在多个环节中直接对机器人进行实时教学和测试。我们对 60 名参与者进行了现场研究,他们与持续学习机器人进行了 300 次互动,每人 5 次。在这项参与者之间的研究中,我们在移动机械手机器人上使用了三种不同的持续学习模型。我们对研究中收集到的数据进行了广泛的定性和定量分析,结果表明,不同用户的教学风格存在显著差异,这表明需要针对他们不同的教学风格进行个性化调整。我们的分析表明,广泛用于测试大多数持续学习机器人模型的受限实验装置是不够的,因为真实用户与持续学习机器人的互动和教学方式多种多样。最后,我们的分析表明,尽管用户对将持续学习机器人应用于日常生活表示担忧,但他们提到,如果进一步改进,持续学习机器人可以帮助老年人和残疾人在家中学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
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.
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