Fluent human-robot collaboration requires a robot teammate to understand, learn, and adapt to the human's psycho-physiological state. Such collaborations require a computing system that monitors human physiological signals during human-robot collaboration (HRC) to quantitatively estimate a human's level of comfort, which we have termed in this research as comfortability index (CI) and uncomfortability index (unCI). Subjective metrics (surprise, anxiety, boredom, calmness, and comfortability) and physiological signals were collected during a human-robot collaboration experiment that varied robot behavior. The emotion circumplex model is adapted to calculate the CI from the participant's quantitative data as well as physiological data. To estimate CI/unCI from physiological signals, time features were extracted from electrocardiogram (ECG), galvanic skin response (GSR), and pupillometry signals. In this research, we successfully adapt the circumplex model to find the location (axis) of 'comfortability' and 'uncomfortability' on the circumplex model, and its location match with the closest emotions on the circumplex model. Finally, the study showed that the proposed approach can estimate human comfortability/uncomfortability from physiological signals.

翻译：流畅的人机协作需要机器人队友理解、学习并适应人类的心理生理状态。此类协作需要构建一个计算系统，在人类-机器人协作（HRC）过程中监测人类生理信号，以定量估计人类的舒适水平，本研究将其定义为舒适度指数（CI）与不舒适度指数（unCI）。通过一项改变机器人行为的人机协作实验，收集了主观指标（惊讶、焦虑、无聊、平静及舒适度）与生理信号。本研究采用情绪环状模型，根据参与者的定量数据及生理数据计算CI。为从生理信号中估计CI/unCI，从心电图（ECG）、皮肤电反应（GSR）及瞳孔测量信号中提取了时间特征。在本研究中，我们成功调整了环状模型，以确定“舒适度”与“不舒适度”在环状模型中的位置（坐标轴），且该位置与环状模型中最接近的情绪相匹配。最后，研究表明所提出的方法能够基于生理信号估计人类舒适度/不舒适度。