Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy
Comparing Robot and Human guided Personalization: Adaptive Exercise Robots are Perceived as more Competent and Trustworthy
复制标题
比较机器人和人类引导的个性化：自适应运动机器人被认为更有能力和值得信赖
DOI：
10.1007/s12369-020-00629-w
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发表时间：
2020
影响因子：
4.7
通讯作者：
F. Kummert
中科院分区：
文献类型：
--
作者：
S. Schneider;F. Kummert
学习和匹配用户的偏好是在长期人-机器人交互(HRI)中实现富有成效的协作的一个重要方面。然而，在如何将机器人的行为与用户的偏好相匹配方面，有不同的技术。机器人可以是可适应的，以便用户可以根据自己的需要改变机器人的行为，或者机器人可以是自适应的，并自动尝试将其行为与用户的偏好相匹配。这两种类型都可能缩小用户偏好和实际系统行为之间的差距。然而，两种方法的机器人自动化水平(LOA)是不同的。要么是用户控制交互，要么是机器人控制。我们提出了一项关于社会辅助机器人(SAR)的不同LOA对用户在锻炼场景中对系统的评价的影响的研究。我们实现了一个在线偏好学习系统和一个用户自适应系统。我们对40名受试者进行了一项主题间设计研究(适应性机器人与适应性机器人)，并报告了我们的定量和定性结果。结果显示，用户对自适应机器人的评价是更有能力、更热情，并报告了更高的联盟。此外，这种增加的联盟在很大程度上受到系统感知能力的影响。这一结果为系统的LOA、用户对系统的感知能力以及与系统的感知联盟之间的关系提供了经验证据。此外，我们提供了概念验证的证据，证明所选择的偏好学习方法(即双汤普森抽样(DTS))适用于在线HRI。
Learning and matching a user’s preference is an essential aspect of achieving a productive collaboration in long-term Human–Robot Interaction (HRI). However, there are different techniques on how to match the behavior of a robot to a user’s preference. The robot can be adaptable so that a user can change the robot’s behavior to one’s need, or the robot can be adaptive and autonomously tries to match its behavior to the user’s preference. Both types might decrease the gap between a user’s preference and the actual system behavior. However, the Level of Automation (LoA) of the robot is different between both methods. Either the user controls the interaction, or the robot is in control. We present a study on the effects of different LoAs of a Socially Assistive Robot (SAR) on a user’s evaluation of the system in an exercising scenario. We implemented an online preference learning system and a user-adaptable system. We conducted a between-subject design study (adaptable robot vs. adaptive robot) with 40 subjects and report our quantitative and qualitative results. The results show that users evaluate the adaptive robots as more competent, warm, and report a higher alliance. Moreover, this increased alliance is significantly mediated by the perceived competence of the system. This result provides empirical evidence for the relation between the LoA of a system, the user’s perceived competence of the system, and the perceived alliance with it. Additionally, we provide evidence for a proof-of-concept that the chosen preference learning method (i.e., Double Thompson Sampling (DTS)) is suitable for online HRI.