自由需要付出代价吗? : 一项探索性研究,探讨负担能力对用户对社交机器人的看法的影响
1The Speech and Hearing Research Group (SpandH), Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.
Frontiers in robotics and AI
|April 2, 2024
概括
将社交机器人的外观,声音和行为与其使用案例对齐,对于用户对人机交互 (HRI) 的满意度至关重要. 本研究探讨了不同的负担能力如何影响用户的感知和满意度.
科学领域:
- 人与机器人的交互 (HRI)
- 社会机器人社会机器人社会机器人
- 人与计算机的交互 (HCI)
- 语音技术 语言技术
背景情况:
- 语音支持的人机交互 (HRI) 的市场正在扩大,但用户满意度仍然很低.
- 一个关键问题是"可居住性差距",即机器人的灵活性增加导致可用性降低.
- 机器人支付的"外观,声音和行为"显著影响用户的感知和满意度.
研究的目的:
- 研究社交机器人的负担能力 (静态和行为) 如何影响用户的感知和满意度.
- 探索将机器人负担与不同用例 (信息化,情感化,混合) 调整机器人的影响.
- 为了加深对负担能力对齐的理解,超越面部和声音的匹配,并纳入机器人的行为.
主要方法:
- 一个探索性实验,参与者与社交机器人直接互动.
- 三种不同的负担能力设置:像成年人一样,像孩子一样和像机器人一样.
- 一种混合方法方法分析156个相互作用样本,以获得定量和定性见解.
主要成果:
- 静态负担 (面部,声音) 显著影响最初的温暖感知.
- 使用案例在互动之前和之后都会影响人们的认知能力和热情.
- 将静态负担与行为负担对齐,对于积极的用户体验至关重要.
结论:
- 机器人负担能力的适当设计,特别是将静态和行为方面与用例对齐,对于增强HRI至关重要.
- 这项研究提供了经验证据,并提出了行为性负担的一般设计原则.
- 这些发现为开发更复杂,更个性化的可负担性设计指南提供了基础,用于支持语音的社交机器人.
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