Impact of Robot Facial-Audio Expressions on Human Robot Trust Dynamics and Trust Repair

📄 arXiv: 2512.13981v1 📥 PDF

作者: Hossein Naderi, Alireza Shojaei, Philip Agee, Kereshmeh Afsari, Abiola Akanmu

分类: cs.RO

发布日期: 2025-12-16

💡 一句话要点

研究机器人面部音频表达对人机信任动态的影响

🎯 匹配领域: 支柱一：机器人控制 (Robot Control) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 人机信任 机器人表达 信任动态 信任修复 多模态交互

📋 核心要点

  1. 现有研究将信任视为静态因素，缺乏对信任在协作事件中动态变化的深入理解。
  2. 本文通过设计受控实验，探讨机器人在成功与失败后的多模态表达对人机信任的影响。
  3. 实验结果显示，机器人成功显著提升信任，而道歉表达可部分恢复信任，且年龄组对信任动态有调节作用。

📝 摘要（中文）

尽管机器人技术和人机协作在AEC行业取得了进展，但信任仍被视为静态因素，缺乏对其在协作过程中变化的指导。本文研究了机器人任务表现及其在结果后的表达反应如何塑造人类信任的动态变化。通过设计受控的实验，使用14项信任感知量表多次测量信任，结果表明，机器人成功会显著提升信任，而失败则会导致信任急剧下降。基于道歉的表达能够部分恢复信任，且不同年龄组的参与者在信任动态上表现出不同的特征，为未来的修复策略提供了基础。

🔬 方法详解

问题定义：本文旨在解决人机信任在协作过程中动态变化的理解不足，现有方法未能有效捕捉信任的变化机制。

核心思路：通过设计包含成功与失败的任务，结合机器人在不同结果后的表达反应，探讨其对人类信任的影响。

技术框架：研究采用了受控的实验设计，包含两个任务（物料交付和信息收集），并使用14项信任感知量表进行多次测量。

关键创新：本研究首次系统性地探讨了机器人表达对人机信任动态的影响，提出了信任修复的多模态表达策略。

关键设计：实验中，机器人在成功后展示“高兴”的表情并确认，在失败后展示“伤心”的表情并请求第二次机会，信任测量在每个任务中进行四次。

📊 实验亮点

实验结果表明，机器人成功时信任显著提升，而失败导致信任急剧下降。道歉表达在物料交付任务中恢复了44%的信任，在信息收集任务中恢复了38%的信任，显示出互动和沟通因素在信任恢复中的重要性。

🎯 应用场景

该研究为机器人在建筑工地的应用提供了重要的理论基础，能够帮助设计更有效的信任修复策略，以适应不同任务需求和用户特征，促进机器人技术的安全和高效采用。

📄 摘要（原文）

Despite recent advances in robotics and human-robot collaboration in the AEC industry, trust has mostly been treated as a static factor, with little guidance on how it changes across events during collaboration. This paper investigates how a robot's task performance and its expressive responses after outcomes shape the dynamics of human trust over time. To this end, we designed a controlled within-subjects study with two construction-inspired tasks, Material Delivery (physical assistance) and Information Gathering (perceptual assistance), and measured trust repeatedly (four times per task) using the 14-item Trust Perception Scale for HRI plus a redelegation choice. The robot produced two multimodal expressions, a "glad" display with a brief confirmation after success, and a "sad" display with an apology and a request for a second chance after failure. The study was conducted in a lab environment with 30 participants and a quadruped platform, and we evaluated trust dynamics and repair across both tasks. Results show that robot success reliably increases trust, failure causes sharp drops, and apology-based expressions partially restores trust (44% recovery in Material Delivery; 38% in Information Gathering). Item-level analysis indicates that recovered trust was driven mostly by interaction and communication factors, with competence recovering partially and autonomy aspects changing least. Additionally, age group and prior attitudes moderated trust dynamics with younger participants showed larger but shorter-lived changes, mid-20s participants exhibited the most durable repair, and older participants showed most conservative dynamics. This work provides a foundation for future efforts that adapt repair strategies to task demands and user profiles to support safe, productive adoption of robots on construction sites.