Robot Movement Uncertainty Determines Human Discomfort in Co-worker Scenarios

Daphné Aéraïz-Bekkis, G. Ganesh, E. Yoshida, N. Yamanobe
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引用次数: 7

Abstract

The long term success of a human-robot interaction will depend on how comfortable and safe a human feels with it. But which feature of a robot's movement determines human comfort? To address this question, here we considered four different models of human discomfort. We then designed an empirical human-robot co-worker task that enables us to both, quantify the discomfort experienced by the human co-worker by analyzing behavioral changes, and examine which model of discomfort explains the changes best. Using this task, we show that the perceived uncertainty in a robot's movement is a key determinant of human discomfort, and we discuss how movement uncertainty can give a unified explanation for the modulation of human comfort with robots, and trust in them, as reported in several previous studies.
机器人运动的不确定性决定了人类在同事场景中的不适
人机交互的长期成功将取决于人类对它的舒适和安全程度。但是机器人运动的哪个特征决定了人类的舒适度呢?为了解决这个问题,这里我们考虑了四种不同的人类不适模型。然后,我们设计了一个经验的人-机器人同事任务,使我们能够通过分析行为变化来量化人类同事所经历的不适,并检查哪种不适模型最能解释这些变化。使用这个任务,我们表明机器人运动中的感知不确定性是人类不适的关键决定因素,我们讨论了运动不确定性如何为人类对机器人的舒适调节和信任提供统一的解释,正如之前的几项研究所报道的那样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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