Högskolan i Skövde

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.

Large knowledge gaps exist regarding how enabling technologies, such as robotics, automation, and AI, impact the digital work environment in the ongoing industrial revolution. While these technologies must align with work tasks, they also have the potential to enhance meaningfulness at work by fulfilling human needs for competence, autonomy, and self-realization. However, the development of laboratory automation has mainly been technology-driven, underscoring the need for a human-centered approach here called Laboratory 5.0. This paper presents an empirical UX evaluation of a semi-automated robot used in inhalator testing by five chemists in a pharmaceutical lab. Data collection included think-aloud protocols, questionnaires, observations, and interviews, analyzed through triangulation. The findings revealed that while the robot streamlined tasks and increased flexibility, it also introduced physical and cognitive work environment challenges, such as ergonomic strain, cognitive load, unclear process feedback, and workflow interruptions. These insights will guide the redesign and implementation of a mobile lab robot, addressing user experience and interaction challenges in the laboratory context. By improving UX in human-robot interaction, this research aims to contribute to a more sustainable, meaningful and engaging digital work environment in Laboratory 5.0, which will benefit chemists and workers in similar domains.

In Industry 4.0 and 5.0, humans and robots share physical and social spaces, making sustainable workplaces essential. While mobile, flexible, and collaborative robots offer new possibilities, challenges remain. Effective human-robot interaction relies on mutual recognition of actions and intentions for efficiency, safety, and smooth collaboration. Awareness of user experience (UX) is growing, as it influences work satisfaction, engagement, and well-being. This paper identifies UX issues when mobile robots are introduced on the shop floor alongside operators and truck drivers. The study, conducted at a large manufacturing plant, examined mobile robots delivering heavy parts to the assembly line and navigating a busy warehouse corridor and production area. Two months after field testing, a post-evaluation took place, with data collected from eight participants, four operators and four truck drivers, who had frequently interacted with the robots. Participants completed a questionnaire and an interview. Key findings show: a) differences between operators and truck drivers, b) higher UX assessments for operators, c) more and greater challenges for truck drivers, and d) neither group seeing major personal benefits but recognizing advantages for the company. Both physical and cognitive work environment problems were identified.

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.

This paper explores the complex and time-critical work practices within operational train traffic in Sweden by reporting on an incident causing an infrastructure breakdown and large traffic disruptions. Based on a workplace study approach, we report on how the control room workers – train traffic controllers and information officers – grasp, make sense of, and handle the consequences of the incident as it unfolds in real-time. We portray how the workers develop and acquire a sense of place in relation to the incident’s severity which is essential for successfully handling the situation. By introducing the ‘sense of place’ concept originally derived from the field of natural resources to the domain of operational train traffic, we provide a deepened understanding of the challenges characterising remote control work from a safety-critical socio-technical systems perspective. Finally, reflections on the application of the ‘sense of place’ concept, safety aspects and directions for future research are provided.

Expectations shape our experience with the world, including our interaction with technology. There is a mismatch between whathumans expect of social robots and what they are actually capable of.Expectations are dynamic and can change over time. We have previ- AQ1ously developed a framework for studying these expectations over timein human-robot interaction (HRI). In this work, we applied the socialrobot expectation gap evaluation framework in an HRI scenario from aUX evaluation perspective, by analyzing a subset of data collected froma larger experiment. The framework is based on three factors of expectation: affect, cognitive processing, as well as behavior and performance. Four UX goals related to a human-robot interaction scenario were evaluated. Results show that expectations change over time with an overallimproved UX in the second interaction. Moreover, even though some UX goals were partly fulfilled, there are severe issues with the conversation between the user and the robot, ranging from the quality of theinteraction to the users’ utterances not being recognized by the robot.This work takes the initial steps towards disentangling how expectations work and change over time in HRI. Future work includes expanding the metrics to study expectations and to further validate the framework.

The benefits of feeding calves more milk are increasingly being recognized by dairy farmers. However, most producers have still not implemented higher feeding plans. The aim of the present study was to gain a deeper understanding of farmer and farm staff attitudes, and the perceptions and factors considered in their decision-making regarding daily milk allowances. We collected data through focus group interviews with dairy farmers, farm managers, and calf-care workers who were selected using purposive and snowball sampling. In total, 40 persons (24 women and 16 men) joined a focus group interview (6 in all, each with 5–8 participants). Interviews were recorded, and recordings were transcribed and analyzed thematically. Participants had contrasting opinions about the minimum, maximum, and recommended daily milk allowances to their calves. Their suggested lowest daily milk allowance to sustain animal welfare ranged from 4 to 8–10 L and the maximum allowance from 6 to 15 L. We found that farmers' and farm staff's choices and recommendations of milk-feeding protocols were influenced by a large number of factors that could be grouped into 4 themes: (1) Life beyond work, (2) Farm facilities and equipment, (3) Care of the calves, and (4) Profitability and production. Participants' considerations were similar and aimed to maximize daily milk allowance based on farm conditions. However, the allowances they described as optimal for their calves often differed from what they considered practically feasible. We found that the care of the calves and the well-being of the owners and the staff was central in the participants' decision-making, but that this care perspective was challenged by the social and economic sustainability of the farm. Most participants fed their calves twice daily and did not think that increasing that number would be practically feasible. Our results indicate that the participants' viewpoints regarding calves were important for their decision-making about milk allowances. We suggest that a more holistic perspective should be used when advising farmers about milk allowances, putting particular emphasis on the caring and social sustainability aspects of the individual farm. 

The industrial evolutions require robots to be able to share physical and social space with humans in such a way that interaction and coexistence arepositively experienced by human workers. A prerequisite is the possibility for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for human-robot interaction are needed that are adapted to operators’ needs and characteristics in the industrial contexts. In this paper, we aim to present various taxonomies of levels of automation, humanrobot interaction, and human-robot collaboration suggested for the envisioned factories of the future. Based on this foundation, we propose a compass direction for continued research efforts which both zooms in and zooms out on how to develop applicable human-robot interaction strategies that are worker-centric in order to obtain effective, efficient, safe, sustainable, and pleasant human-robot collaboration and coexistence.

The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for humanrobot interaction are needed that are adapted to operators’ needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.