With the vision of Industry 4.0 and cobots, working conditions in industrial settings are starting to change. We review related literature from the fields of human–robot interaction, work and organizational psychology, and sociology of work, as well as an exemplary project case study, and identify research gaps regarding the implications of cobots for work environments. We argue that we are in a transition phase from automation to actual collaboration with robots in manufacturing, and that this will open up a new problem space for investigations, in which a practice lens will be crucial. Based on this, we propose a research agenda for social practice and workplace studies to explore the sociotechnical environment of Industry 4.0 involving cobots at the individual, team, and organizational levels.

Humanoid social robots have an increasingly prominent place in today’s world. Their acceptance in social and emotional human–robot interaction (HRI) scenarios depends on their ability to convey well recognized and believable emotional expressions to their human users. In this article, we incorporate recent findings from psychology, neuroscience, human–computer interaction, and HRI, to examine how people recognize and respond to emotions displayed by the body and voice of humanoid robots, with a particular emphasis on the effects of incongruence. In a social HRI laboratory experiment, we investigated contextual incongruence (i.e., the conflict situation where a robot’s reaction is incongrous with the socio-emotional context of the interaction) and cross-modal incongruence (i.e., the conflict situation where an observer receives incongruous emotional information across the auditory (vocal prosody) and visual (whole-body expressions) modalities). Results showed that both contextual incongruence and cross-modal incongruence confused observers and decreased the likelihood that they accurately recognized the emotional expressions of the robot. This, in turn, gives the impression that the robot is unintelligent or unable to express “empathic” behaviour and leads to profoundly harmful effects on likability and believability. Our findings reinforce the need of proper design of emotional expressions for robots that use several channels to communicate their emotional states in a clear and effective way. We offer recommendations regarding design choices and discuss future research areas in the direction of multimodal HRI.

Standardized metrics for assessing the success of robots is a necessity for a research field to compare and validate results. The Godspeed Questionnaire Series (GQS) is one of the most frequently used questionnaires in the field of Human-Robot Interaction (HRI) with over 160 citations as of October 2014. In this paper, we present a meta analysis of studies that used the GQS. The HRI community uses a large variety of robotic platforms and only the NAO robot seems to be used by multiple research groups. A qualitative meta analysis of 18 NAO studies reveals accumulated findings on perceived intelligence, likability, and anthropomorphism, but also reveals contradictions on how the robot's behaviour and task context impact GQS ratings. The paper closes with a reflection on how added value of data analysis and presentation could be achieved for the HRI community in future.