Högskolan i Skövde

Collaborative robotics is a very promising technology for many industrial processes, including e.g., manufacturing, logistics, or construction. This new technology are also changing the environment for workers in industry. Research on human-robot interaction (HRI) will be crucial for enhancing the operator’s work conditions and wellbeing, as well as production performance. In that regard, human factors, with a special emphasis on cognitive ergonomics are fundamental to implementing safe, fluent, and efficient collaborative applications.

This Research Topic gathers a range of contributions on the study of Human Factors and Cognitive ergonomics in user-centered and collaborative applications in industrial settings. Here, we summarize these studies from the perspective of three pivotal areas impacted by collaborative robotics: workers’ safety, performance, and wellbeing.

Collaboration between robots and human workers constitutes an important part of the shift towards human-centric production processes. While collaborative robots have increased flexibility and enabled compact production environments, real-time intent recognition and joint actions in human-robot collaboration (HRC) continue to pose challenges, particularly in dynamic target selection and coordination. Conventional HRC systems rely on manual inputs or predefined gestures. This study explores the feasibility of gaze-based interaction as a natural and intuitive control mechanism for close-proximity HRC in dynamic industrial settings. The approach is demonstrated in the form of a prototype workstation for wire harness assembly on a car bumper, where the human and the robot collaborate in real time using eye gaze-based dynamic target selection and tight action coordination. The human eye gaze was recorded in real-time using eye-tracking glasses, and the forward-facing scene camera of the glasses matched it to upcoming assembly targets for the robot. The system was evaluated based on detection accuracy and response time. Results indicate that the human eye gaze can constitute a powerful cue for robotic target selection and action coordination in HRC-based assembly. The YOLOv8-based object detection model achieved average target recognition confidence above 60%, despite sensitivity to lighting variations and dataset composition. These findings support the viability of gaze-based robotic control in industrial assembly, demonstrating its potential to enhance operator ergonomics, streamline task execution, and improve overall system responsiveness in dynamic work environments

Collaborative robotics is a very promising technology for many industrial processes, including e.g., manufacturing, logistics, or construction. This new technology are also changing the environment for workers in industry. Research on human-robot interaction (HRI) will be crucial for enhancing the operator’s work conditions and wellbeing, as well as production performance. In that regard, human factors, with a special emphasis on cognitive ergonomics are fundamental to implementing safe, fluent, and efficient collaborative applications.

This Research Topic gathers a range of contributions on the study of Human Factors and Cognitive ergonomics in user-centered and collaborative applications in industrial settings. Here, we summarize these studies from the perspective of three pivotal areas impacted by collaborative robotics: workers’ safety, performance, and wellbeing.

Increasing age is often associated with a decline in the sense of health, well-being and quality of life, which can contribute to social isolation and a sense of loneliness. Demographic changes and the transfer of care from institution to home raise the need for innovative solutions to preserve and promote well-being and quality of life. There is a growing interest in using robots in care for older adults. Despite its potential to improve well-being, using robots in care for older adults remains limited. This paper adheres to the importance of having a user-centred approach as the focal point. The purpose is to explore social robots as a means to enhance the quality of life, and the research question is: What values can a social robot fill in everyday life for older adults? Qualitative data was collected in a workshop with eight older adults. The workshop consisted of two phases: 1) interaction with Pepper and 2) responding to a questionnaire followed by reflection and discussion individually and group-wise. Thematic analysis was conducted, which generated three overall themes: variation in the older adults’ first encounter experiences with Pepper; the participants’ main challenges in daily life at home; the potential value of having a social robot at home; and aspects affecting experienced value. The insights from the workshop resulted in the research project Social Robots in Home Environments for Older Persons’ Quality of Life - Needs, Opportunities and Obstacles (RO-LIV). 

It has been shown that interaction methods such as body language and gestures in socially assistive robots (SAR) contribute to engagement, attention, and entertainment value. Studies in social cognition emphasize the significance of body language for facilitating interaction in social exchanges. Inspired by these results, an independent group experiment (N=45) was designed to investigate how body language, as an interaction method in SAR, affects perceived social presence. Participants engaged in semi-structured conversations with the social robot Pepper, equipped with a ChatGPT-based dialogue system with, or without, body language. Perceived social presence was retrieved through the Almere questionnaire. Contrary to our hypothesis, the results did not show any significant differences in perceived social presence. Detailed analysis did however show that the interactive condition enhanced the feeling of being seen and tended to make the robot more entertaining. The lack of support for the hypothesis suggests that the robot's body language might be less significant than previously thought, possibly due to method and design factors, as well as the robot's advanced dialogue system. This study highlights the potential of large language models for SAR and could indicate that some aspects of the robot’s design might overshadow other aspects.

The field Human-Robot Interaction (HRI) involves new forms of social interactions that are dependent on the many and different earlier expectations of humans. In this study, the impact of linguistic framing on students' expectations and user experiences when being introduced to social robots in an educational setting were investigated. An empirical case study involved the social robot Pepper and 10 students aged 16–19. The introduction to Pepper utilized two forms of linguistic framing: positive and negative terms. Pre- and post-interaction interviews were conducted to assess the students' expectations and experiences. Assessments to measure negative attitudes toward robots and user experiences were conducted using the NARS and Godspeed questionnaires. Furthermore, filmed observations of the students' interactions with Pepper were used to provide additional insights. Results of the study showed that students' expectations and experiences varied depending on the type of introduction and linguistic framing utilized. While none of the differences among the questionnaire responses were statistically significant, the trends were in line with the clear results from the interviews and observations.

The human–robot interaction (HRI) field goes beyond the mere technical aspects of developing robots, often investigating how humans perceive robots. Human perceptions and behavior are determined, in part, by expectations. Given the impact of expectations on behavior, it is important to understand what expectations individuals bring into HRI settings and how those expectations may affect their interactions with the robot over time. For many people, social robots are not a common part of their experiences, thus any expectations they have of social robots are likely shaped by other sources. As a result, individual expectations coming into HRI settings may be highly variable. Although there has been some recent interest in expectations within the field, there is an overall lack of empirical investigation into its impacts on HRI, especially in-person robot interactions. To this end, a within-subject in-person study () was performed where participants were instructed to engage in open conversation with the social robot Pepper during two 2.5 min sessions. The robot was equipped with a custom dialogue system based on the GPT-3 large language model, allowing autonomous responses to verbal input. Participants’ affective changes towards the robot were assessed using three questionnaires, NARS, RAS, commonly used in HRI studies, and Closeness, based on the IOS scale. In addition to the three standard questionnaires, a custom question was administered to capture participants’ views on robot capabilities. All measures were collected three times, before the interaction with the robot, after the first interaction with the robot, and after the second interaction with the robot. Results revealed that participants to large degrees stayed with the expectations they had coming into the study, and in contrast to our hypothesis, none of the measured scales moved towards a common mean. Moreover, previous experience with robots was revealed to be a major factor of how participants experienced the robot in the study. These results could be interpreted as implying that expectations of robots are to large degrees decided before interactions with the robot, and that these expectations do not necessarily change as a result of the interaction. Results reveal a strong connection to how expectations are studied in social psychology and human-human interaction, underpinning its relevance for HRI research.

Proactive eye-gaze (PEG) is a behavioural pattern where eye fixations precede actions, such as reaching. With the proliferation of eye-tracking technology, PEG shows promise for predicting human actions, which has many applications, for example, within industrial human-robot collaboration (HRC). This study investigates PEG in repetitive assembly tasks. Eye-tracking data from four experienced workers were recorded and analysed. The study recorded 57 assembly sessions, identifying 3793 fixations, of which 35% were proactive gazes. The mean PEG interval was 795 ms. Contrary to the hypothesis, PEG was found to be as strong, if not stronger, in repetitive tasks compared to previous studies investigating PEG in other contexts. These findings suggest PEG could be a reliable predictor of worker actions in repetitive tasks, enhancing coordination in HRC.