An experimental focus on learning effect and interaction quality in human–robot collaboration

Abstract

In the landscape of the emerging Industry 5.0, human–robot collaboration (HRC) represents a solution to increase the flexibility and reconfigurability of production processes. Unlike classical industrial automation, in HRC it is possible to have direct interaction between humans and robots. Consequently, in order to effectively implement HRC it is necessary to consider not only technical aspects related to the robot but also human aspects. The focus of this paper is to expand on previous results investigating how the learning process (i.e., the experience gained through the interaction) affects the user experience in the HRC in conjunction with different configuration factors (i.e., robot speed, task execution control, and proximity to robot workspace). Participants performed an assembly task in 12 different configurations and provided feedback on their experience. In addition to perceived interaction quality, self-reported affective state and stress-related physiological indicators (i.e., average skin conductance response and heart rate variability) were collected. A deep quantitative analysis of the response variables revealed a significant influence of the learning process in the user experience. In addition, the perception of some configuration factors changed during the experiment. Finally, a significant influence of participant characteristics also emerged, auguring the necessity of promoting a human-centered HRC.