While trust in human-robot interaction is increasingly recognized as necessary for the implementation of social robots, our understanding of regulating trust in human-robot interaction is yet limited. In the current experiment, we evaluated different approaches to trust calibration in human-robot interaction. The within-subject experimental approach utilized five different strategies for trust calibration: proficiency, situation awareness, transparency, trust violation, and trust repair. We implemented these interventions into a within-subject experiment where participants (N=24) teamed up with a social robot and played a collaborative game. The level of trust was measured after each section using the Multi-Dimensional Measure of Trust (MDMT) scale. As expected, the interventions have a significant effect on i) violating and ii) repairing the level of trust throughout the interaction. Consequently, the robot demonstrating situation awareness was perceived as significantly more benevolent than the baseline.

翻译：尽管人机交互中的信任日益被认为是社会机器人实现所必需的，但我们对人机交互中信任调控机制的理解仍十分有限。本实验评估了人机交互中信任校准的不同方法。采用被试内实验设计，我们运用了五种不同的信任校准策略：能力水平、情境意识、透明度、信任违背及信任修复。我们将这些干预措施纳入被试内实验，让参与者（N=24）与社会机器人组队进行协作游戏。使用多维度信任量表测量每个环节后的信任水平。正如预期，这些干预措施对交互过程中的信任违背与修复具有显著影响。其中，展现情境意识的机器人在感知友善度上显著高于基准水平。