SGER: Investigating the Utility of Affect Mechanisms in Mixed Human-Robot Teams

在人类中，情感与认知深度交织在一起，可以影响问题的解决和决策策略，或者对社会情况的评估，等等。对于在团队中与人类一起工作的机器人来说，这意味着意识到人类的影响，并基于人类对如何应对人类影响的预期来调整它们的行为，这不仅可能导致更自然的互动，而且还可以提高人类-机器人团队的表现。目前，只有一项初步研究试图在人-机器人混合团队中客观量化机器人情感表达对任务绩效的影响，本项目将为机器人体系结构中使用情感机制的实用性收集进一步的证据。具体地说，该项目将调查有选择地使用机器人产生的口语输出的情感调制，以应对人类因高认知负荷而产生的压力，无论是在人类的声音中检测到，还是通过连接到人类受试者的生理传感器检测到，是否可以改善人类-机器人团队的表现。此外，还将确定结果是否取决于互动的频率和互动距离，将面对面的互动与通过视频链接进行的远程互动进行比较。

Affect is deeply intertwined with cognition in humans and can influence problem solving and decision making strategies, or evaluations of social situations, among many others. For robots working with humans in teams this means that being aware of human affect and adapting their behavior based on human expectations about how to respond to human affect might not only lead to more natural interactions, but also improve the performance of human-robot teams. Currently, there is only one preliminary study that attempts to quantify objectively the effect of robotic affect expression on task performance in a mixed human-robot team.This project will build collect further evidence for the utility of using affect mechanisms in robotic architectures. Specifically, the project will investigate whether selectively using affect modulations of spoken language output generated by the robot in response to human stress due to high cognitive load, detected either in the human voice or via physiological sensors attached to human subjects, can improve the performance of human-robot teams. Moreover, it will be determined if the outcomes depend on the frequency of interactions as well as the interaction distance, comparing face-to-face interactions with remote interactions via a video link.