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

情感与人类的认知深深交织在一起，可以影响问题解决和决策策略，或对社会状况的评估等。 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根据人类对如何应对人类影响的期望来调整自己的行为，这不仅可以带来更自然的互动，还可以提高人类-机器人团队的表现。 目前，只有一个初步的研究，试图客观地量化机器人的情感表达对任务绩效的影响，在一个混合的人-机器人团队。这个项目将建立收集进一步的证据，在机器人架构中使用情感机制的效用。 具体来说，该项目将调查是否选择性地使用由机器人响应人类压力而产生的口语输出的情感调制，由于人类的声音或通过附着在人类主体上的生理传感器检测到的高认知负荷，可以提高人类-机器人团队的表现。 此外，将确定结果是否取决于交互的频率以及交互距离，将面对面的交互与通过视频链接的远程交互进行比较。

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.