论文标题
利用语音减少对人形社会机器人的信任的丧失
Using Speech to Reduce Loss of Trust in Humanoid Social Robots
论文作者
论文摘要
我们从两个在线人机互动实验中介绍了数据，其中227位参与者观看了人类机器人的视频，表现出有缺陷或非故障行为，同时保持沉默或说话。要求参与者评估他们对机器人的信任度的看法，以及其可爱，动画和感知的情报。结果表明，虽然一个非故障机器人达到了最高的信任，但看似有故障的机器人可以说，几乎可以完全减轻信任的损失，而这些信任的损失否则会出现错误的行为。我们认为，这种缓解与感知的智能的增加相关，这在存在语音时也可以看到。
We present data from two online human-robot interaction experiments where 227 participants viewed videos of a humanoid robot exhibiting faulty or non-faulty behaviours while either remaining mute or speaking. The participants were asked to evaluate their perception of the robot's trustworthiness, as well as its likeability, animacy, and perceived intelligence. The results show that, while a non-faulty robot achieves the highest trust, an apparently faulty robot that can speak manages to almost completely mitigate the loss of trust that is otherwise seen with faulty behaviour. We theorize that this mitigation is correlated with the increase in perceived intelligence that is also seen when speech is present.
