在人机交互中引发和识别以任务为导向的对话者混乱
Na Li1, Robert Ross1
1School of Computer Science, Technological University, Dublin, Ireland.
Frontiers in robotics and AI
|December 6, 2023
概括
研究人员研究了在人机交互期间用户的困惑. 他们发现,视线,头姿势和语音分析等多模式信号可以有效地检测用户的困惑,使机器人能够调整他们的反应.
科学领域:
- 人与机器人的交互
- 情感计算是一种情感计算.
- 社会机器人社会机器人社会机器人
背景情况:
- 有效的人机交互 (HRI) 需要了解用户的情绪和态度状态.
- 检测用户的困惑对于机器人来说至关重要,以适应他们的对话策略.
研究的目的:
- 在人机交互过程中系统地触发,解释和检测用户混乱状态.
- 识别与用户困惑相关的多式联络行为线索.
主要方法:
- 一个"奥兹魔法师" (WoZ) 研究,使用Pepper机器人和81名参与者.
- 收集的视听数据和参与者调查数据.
- 分析了多模式信号:眼睛的凝视,头部姿势,面部情绪,言语和沉默的持续时间.
主要成果:
- 在混乱状态中观察到参与者的行为有显著差异.
- 在诱导混和自我报告的混得分之间发现了强烈的相关性.
- 多式联网信号与用户混水平有效相关.
结论:
- 可观察到的多式联运特征可以可靠地表明用户在HRI中的困惑.
- 这项研究为感受意识的社交机器人提供了基础.
- 该研究提供了一种方法,数据集和分析,用于检测HRI中的混.
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