社会机器人的面部人类形态可信度度量:一种混合方法
Yao Song1,2,3, Ameersing Luximon4, Yan Luximon3
1Digital Convergence Laboratory of Chinese Cultural Inheritance and Global Communication, Sichuan University, Chengdu 610065, China.
Biomimetics (Basel, Switzerland)
|August 25, 2023
概括
研究人员开发了一种新的尺度,即面部人类形态对社会机器人的可信度 (FATSR-17),以衡量机器人的面部看起来有多可信. 这个工具有助于设计更值得信赖的社交机器人.
科学领域:
- 人与机器人的交互
- 机器人技术 机器人技术 机器人技术
- 心理学 心理学 心理学
背景情况:
- 社会机器人越来越多地融入人类环境.
- 测量用户对机器人外观的信任,特别是在初次接触时,至关重要,但研究不足.
- 机器人中的人类形象显著影响用户的感知和接受.
研究的目的:
- 开发和验证一个可靠的测量尺度面部可信度在人形社会机器人.
- 解决量化用户对机器人面部可信度认知的差距.
- 为机器人设计师提供一个工具,以增强用户的信任.
主要方法:
- 一种混合深度卷积方法,将众包与数据收集相结合.
- 深度卷积和因子分析用于数据处理和规模开发.
- 代检查和细化以确保尺度的心理特征.
主要成果:
- 对社会机器人的面部人类形态可信度 (FATSR-17) 尺度的开发.
- 该FATSR-17规模包括四个维度的17个项目:伦理问题,能力,积极影响和人形.
- 该尺度通过严格的测试证明了它的可靠性和有效性.
结论:
- FATSR-17尺度为评估机器人面部可信度提供了一个结构化的工具包.
- 这项研究有助于设计社会机器人,从而获得更大的用户信任.
- 这些发现有助于推进人机交互和机器人设计原则的领域.
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