Högskolan i Skövde

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

Studies in visual cognition highlight the importance of visual, spatial, and auditory cues in influencing human attention. Such cues often tend to be indicative of actions or events, thereby serving as predictive indicators in both passive observation as well as in interactive engagement. Our research focuses on visual attention in passive observation, particularly examining the manner in which visual, spatial, and auditory cues —henceforth visuoauditory (shorthand) cues— influence attention on everyday multimodal interaction. We systematically develop a visuoauditory event model for investigating visual attention in naturalistic embodied settings. Rooted in this event model, we explore the influence of five select visuoauditory cues —namely, speaking, gaze, relative motion, hand action, and visibility— on visual attention. Our analysis utilizes eye-tracking data from  participants observing  carefully designed naturalistic event scenarios and correlating their attentional metrics with the select visuoauditory cues in the backdrop of the developed event model. Findings reveal strong associations between attention and both intra-modal (irrespective of other cues) and cross-modal (combined with other cues) cueing effects, thereby highlighting the nuanced interplay amongst the cues influencing attentional patterns. We develop a systematic and generalized method for analyzing interactions and behavioral parameters, thereby characterizing the impact of visuoauditory cues on attentional dynamics. Our methodology, combined with the obtained insights into the attentional cueing effects, provides an analytical framework explicating the manner in which everyday (interactive) events directly drive attention under naturalistic conditions. This facilitates not only the precise modeling of behavior and attention allocation but also offers a high-level ‘experimental lens’ for examining interactions in relation to behavioral parameters. Taken together, our methodological and behavioral findings are well-positioned to benefit multiple fields, particularly by advancing human-centered design across diverse application domains. Lastly, towards promoting open-science and for wider dissemination, the complete experimental basis of this research —e.g., event-scenarios, high-quality annotated data, data-set supplementary— have been independently documented together with instructions to use and access experimental data.

Dataset Infos [76] https://codesign-lab.org/cognitive-vision/multimodal-cues.

The use of social robots in many sectors of society is predicted to progressively increase. Therefore, exploring how expectations play a role in and change users’ experiences when interacting with these robots over time is necessary. From an interpretative and insight-driven approach, our aim was to explore how humans experience in-person interactions with the social robot Pepper, which was equipped with the OpenAI GPT-3 language model. Qualitative data from 62 video recordings of the interactions with Pepper and post-test interviews were collected from 31 participants. An experiential reflexive thematic analysis was applied. The main findings include various levels of interaction quality, different interaction strategies, and elements influencing the users’ expectations and experiences, which were synthesized into a coherent framework. It appears that the participants adapted their interaction strategies based on their expectations and the perceived capability of the robot, which influenced their experiences. This reveals that positive user experience is not solely determined by interaction quality, showing the interplay among these aspects when interacting with a social robot. To conclude, our findings underscore the intricate nature of the role of user expectations and experiences in social human–robot interaction. The work adds complementary qualitative approaches to the Human–Robot Interaction community to provide additional insights on interacting with social robots.

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.

The use of social robots in therapy for children with autism has been explored for more than 20 years, but there still is limited clinical evidence. The work presented here provides a systematic approach to evaluating both efficacy and effectiveness, bridging the gap between theory and practice by targeting joint attention, imitation, and turn-taking as core developmental mechanisms that can make a difference in autism interventions. We present two randomized clinical trials with different robot-assisted therapy implementations aimed at young children. The first is an efficacy trial (n = 69; mean age = 4.4 years) showing that 12 biweekly sessions of in-clinic robot-assisted therapy achieve equivalent outcomes to conventional treatment but with a significant increase in the patients? engagement. The second trial (n = 63; mean age = 5.9 years) evaluates the effectiveness in real-world settings by substituting the clinical setup with a simpler one for use in schools or homes. Over the course of a modest dosage of five sessions, we show equivalent outcomes to standard treatment. Both efficacy and effectiveness trials lend further credibility to the beneficial role that social robots can play in autism therapy while also highlighting the potential advantages of portable and cost-effective setups. Robot-assisted therapy for children with autism is shown to be equally efficacious and more engaging than conventional therapy.

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.