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.

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.

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.

Extended Reality (XR) is a powerful tool to create new and engaging learning environments. As the technology matures, it opens possibilities for professional training programs where information can be situated in the environment, giving detailed guidance to the user. While this detailed guidance has the potential to help users quickly complete complex tasks, recent research from cognitive psychology indicates that active memory retrieval, often referred to as the testing effect, plays a key role in learning. Specifically, increased support during learning is associated with reduced memory retrieval, with negative effects on long-term retention. While these findings are robust for tasks such as word-pair and image learning, less is known about the impact of the testing effect on motor-skill learning of the type often exercised in XR. In this paper, we present the results of a literature review looking at the state of research on the testing effect related to motor-skill learning and retention. While few articles present findings on the testing effect in motor learning, existing results indicate that the impact of the testing effect on motor learning is similar to non-motor learning; however, more research is necessary in order to draw any strong conclusions.

Socially Assistive Robots (SAR) has been suggested as an important technology in the shift of care from institution to home environments, and has been shown to nr effective in addressing loneliness and social isolation among older adults (Lee et al. 2023., Lorenz et al., 2016, Shishehgar et al., 2018). In a newly started research project RO-LIV, we employ a user experience design approach, involving older adults as co-designers and engaged actors, in order to identify needs, solutions, and obstacles for integrating socially assistive robots into older adults' homes. The research is organized into three work packages: Needs Analysis, Current Situation Analysis, and Conditions and Obstacles for Integration into the Home Environments. The expected results include a road map for the integration of socially assistive robots into older adults' homes, informed by a nuanced understanding of user needs and preferences. Overall, we emphasize the importance of adopting a user-centered approach in human-robot interaction research, particularly when designing solutions for older adults. By involving older adults in the design process and addressing their diverse needs, researchers can develop robotic systems that are address real user needs, are socially acceptable, and has an increased potential for adoption and impact on quality of life.