VR and GUI based Human-Robot Interaction Behavior Collection for Modeling the Subjective Evaluation of the Interaction Quality
VR and GUI based Human-Robot Interaction Behavior Collection for Modeling the Subjective Evaluation of the Interaction Quality
复制标题
基于 VR 和 GUI 的人机交互行为采集，用于建模交互质量的主观评价
DOI：
10.1109/sii52469.2022.9708824
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Inamura Tetsunari
中科院分区：
文献类型：
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作者：
Mizuchi Yoshiaki;Iwami Kouichi;Inamura Tetsunari
制定人机交互质量的评价标准是提高对话系统和交互式社交机器人功能的重要研究课题。QoHRI评价的理想方法是由不同的人类评价者使用问卷进行主观评价。然而，这是耗时的，不适用的评分方法的机器人比赛和自主学习的交互式机器人，因此，我们专注于数据驱动的方法，通过近似的主观评价结果的基础上的HRI行为数据的评价标准。我们的第一个研究问题是：我们如何收集各种各样的交互行为数据，包括良好和不良质量的交互？为了收集各种交互数据，同时调节QoHRI，我们提出了一个基于VR和GUI的交互生成器，其中人类和机器人可以相互交互，这是本研究的第一个贡献。为了研究所提出的系统是否可以覆盖各种各样的交互，我们引入了一个度量的交互数据集覆盖的主观评价近似的QoHRI的角度。我们通过比较机器人竞赛领域的三个数据集来验证所提出的系统的有用性，这是本研究的第二个贡献。
Formulating the evaluation criteria for the quality of human-robot interaction (QoHRI) is an important research topic for improving the functions of dialogue systems and interactive social robots. An ideal method for QoHRI evaluation is the subjective evaluation using questionnaires with various human evaluators. However, it is time-consuming and inapplicable for the scoring method for robot competitions and autonomous learning by interactive robots; hence we focus on a data-driven approach that models the evaluation criterion by approximating the subjective evaluation results based on the HRI behavior data. Our first research question is: How can we collect a wide variety of interaction behavior data that include both good- and bad-quality interactions? To collect various interaction data while moderating the QoHRI, we propose a VR and GUI-based interaction generator in which humans and robots can interact with each other, which is the first contribution of this study. To investigate whether the proposed system can cover a wide variety of interactions, we introduce a metric of interaction datasets coverage from the perspective of the subjective evaluation approximation of QoHRI. We validated the usefulness of the proposed system by comparing three datasets in a robot competition domain, which is the second contribution of this study.