Collaborative robots (cobots) and AI technologies are increasingly adopted in industrial settings to enhance productivity and efficiency. While cobots equipped with AI capabilities can enable more collaborative work between people and machines, they also face worker acceptance challenges. Understanding future workers’ perceptions, attitudes, and experiences with cobots and conversational AIs can inform robot designers and developers to design systems that promote collaboration, trust, and acceptance. In this study, we gathered quantitative and qualitative data from 37 participants enrolled in a vocational training program for industrial factory workers, who interacted with an AI-empowered, voice-enabled cobot during a simulated smart-factory assembly task and visited an art exhibition featuring industrial robots and cobots. While these participants are not currently employed in factories, they are considered proxy users —individuals with relevant domain knowledge and training who represent future factory workers. The art exhibition functioned as a design probe to illicit discussion and prompt critical reflection about automation and the role of artificial emotions in HRI. The smart-factory task offered participants a concrete example of how AI-empowered virtual assistants might be combined with cobots on the factory floor. In contrast with some of the HRI literature, participants expressed a strong preference for robots without emotional displays and social behaviors, challenging the view that anthropomorphism and human-like emotions promote robot acceptance. Based on our study, we propose design recommendations for developing AI-empowered, voice-enabled cobots based on five themes generated from the qualitative data.

Integrating UX into industry practices is a well-researched topic particularly for software companies. However, little is known about how UX integration and adoption ‘works’ within the robotics industry. This study identifies behaviours impeding UX adoption at the individual, team, and organisational level within a company developing robots. We carried out a one-year long Action Research study in a large, international company during design and development of a service robot. Based on a UX maturity assessment, we carried out six interventions, where the researcher and practitioners worked deliberately with UX competence-development and the company's UX culture. Together we identified six barriers: achieving appropriate confidence levels, trust in UX, commitment and support structures, trade-offs in UX, handover practices, and UX management. To address these barriers, we developed and tested 21 strategies to help foster good UX practices and promote a ‘UX-friendly’ culture that empowers non-UX professionals to drive user-involved initiatives themselves.

Professional service robots are increasingly being deployed in public places, which thus increases user exposure. However, we lack an empirical understanding of complex encounters taking place in dynamic and often crowded environments as well as how people overcome breakdowns during unguided interaction with a robot in a real-world scenario. In this paper, we conducted a covert, digital ethnographic study analyzing 104 user-generated YouTube videos focusing on people’s unguided interactions with robots in several public places. We identified several types of interaction breakdowns pertaining to someone (person-initiated interaction breakdown, IB) or something (environmental disturbances, ED) having a direct, negative effect on an ongoing unguided interaction. Our findings have implications for the design and development of service robots facing multi-user scenarios entertaining active (primary and secondary) users, inactive (commentators and observers) ‘users’, and Incidentally Co-present Persons (InCoPs) . Furthermore, we contribute to and built on the limited prior use of YouTube videos and digital ethnography in HRI research, thereby demonstrating its effectiveness in studying unguided interactions in public places, while supplementing and adding to the existing knowledge base of service robots in public places.

This study investigates the subjective experience of interacting with a social robot at Aalborg Airport (AAL) by conducting a field study where 23 attributes in Human-Robot Interaction (HRI) were elicited. During two tests, Danish travellers were recruited by a remote controlled Double robot, which offered four wayfinding options specific for AAL. In the first test 30 subjects participated in a semi-structured interview about their experience. Observations and the subjects' statements were interpreted and coded using an affinity diagram. The affinity diagram resulted in 10 superordinate categories from which the 23 attributes were elicited and developed into scales. The scales were used in the second test at AAL, where 43 subjects rated HRI. The ratings were analysed with Principal Component Analysis (PCA). The developed scales presented in this paper might be used by robot designers for specific contexts and potentially for tailored user experience evaluations.