Perugia Giulia, Paetzel-Prüsmann Maike, Alanenpää Madelene, Castellano Ginevra
Uppsala Social Robotics Lab, Department of Information Technology, Uppsala University, Uppsala, Sweden.
Computational Linguistics, Department of Linguistics, University of Potsdam, Potsdam, Germany.
Front Robot AI. 2021 Apr 7;8:645956. doi: 10.3389/frobt.2021.645956. eCollection 2021.
Over the past years, extensive research has been dedicated to developing robust platforms and data-driven dialog models to support long-term human-robot interactions. However, little is known about how people's perception of robots and engagement with them develop over time and how these can be accurately assessed through implicit and continuous measurement techniques. In this paper, we explore this by involving participants in three interaction sessions with multiple days of zero exposure in between. Each session consists of a joint task with a robot as well as two short social chats with it before and after the task. We measure participants' gaze patterns with a wearable eye-tracker and gauge their perception of the robot and engagement with it and the joint task using questionnaires. Results disclose that aversion of gaze in a social chat is an indicator of a robot's uncanniness and that the more people gaze at the robot in a joint task, the worse they perform. In contrast with most HRI literature, our results show that gaze toward an object of shared attention, rather than gaze toward a robotic partner, is the most meaningful predictor of engagement in a joint task. Furthermore, the analyses of gaze patterns in repeated interactions disclose that people's mutual gaze in a social chat develops congruently with their perceptions of the robot over time. These are key findings for the HRI community as they entail that gaze behavior can be used as an implicit measure of people's perception of robots in a social chat and of their engagement and task performance in a joint task.
在过去几年中,大量研究致力于开发强大的平台和数据驱动的对话模型,以支持长期的人机交互。然而,对于人们对机器人的认知以及与机器人的互动如何随时间发展,以及如何通过隐式和连续测量技术对这些进行准确评估,我们知之甚少。在本文中,我们通过让参与者参与三个交互环节来探讨这一问题,每个环节之间有多天的零接触。每个环节包括与机器人共同完成一项任务,以及在任务前后与机器人进行两次简短的社交聊天。我们使用可穿戴式眼动仪测量参与者的注视模式,并通过问卷评估他们对机器人的认知、与机器人的互动以及对共同任务的参与度。结果表明,在社交聊天中回避注视是机器人怪异感的一个指标,并且在共同任务中人们注视机器人的时间越长,他们的表现就越差。与大多数人机交互文献不同,我们的结果表明,在共同任务中,注视共同关注的对象而非机器人伙伴,是参与度最有意义的预测指标。此外,对重复交互中注视模式的分析表明,随着时间的推移,人们在社交聊天中的相互注视与他们对机器人的认知同步发展。这些是人机交互领域的关键发现,因为它们意味着注视行为可以作为一种隐式测量方法,用于衡量人们在社交聊天中对机器人的认知,以及他们在共同任务中的参与度和任务表现。