Högskolan i Skövde

The field of Human-Robot Interaction (HRI) is progressively maturing into a distinct discipline with its own research practices and traditions. Aiming to support this development, we analyzed how ethical conduct was reported and discussed in HRI research involving human participants. A literature study of 73 papers from three major HRI publication outlets was performed. The analysis considered how often the following five principles of ethical conduct were reported: ethical board approval, informed consent, data protection and privacy, deception, and debriefing. These five principles were selected as they belong to all major and relevant ethical guidelines for the HRI field. The results show that overall, ethical conduct is rarely reported, with four out of five principles mentioned in less than one third of all papers. The most frequently mentioned aspect was informed consent, which was reported in 49% of the articles. In this work, we aim to stimulate increased acknowledgment and discussion of ethical conduct reporting within the HRI field.

It is acknowledged that humans expect social robots to interact in a similar way as in human-human interaction. To create successful interactions between humans and social robots, it is envisioned that the social robot should be viewed as an interaction partner rather than an inanimate thing. This implies that the robot should act autonomously, being able to ‘perceive’ and ‘anticipate’ the human’s actions as well as its own actions ‘here and now’. Two crucial aspects that affect the quality of social human-robot interaction is the social robot’s physical embodiment and its performed behaviors. In any interaction, before, during or after, there are certain expectations of what the social robot is capable of. The role of expectations is a key research topic in the field of Human-Robot Interaction (HRI); if a social robot does not meet the expectations during interaction, the human (user) may shift from viewing the robot as an interaction partner to an inanimate thing. The aim of this work is to unravel the role and relevance of humans’ expectations of social robots and why it is important area of study in HRI research. Moreover, I argue that the field of HRI can greatly benefit from incorporating approaches and methods from the field of User Experience (UX) in its efforts to gain a deeper understanding of human users’ expectations of social robots and making sure that the matching of these expectations and reality is better aligned.

Social robots are designed in manners that encourage users to interact and communicate with them in socially appropriate ways, which implies that these robots should copy many social human behaviors to succeed in social settings. However, this approach has implications for what humans subsequently expect from these robots. There is a mismatch between expected capabilities and actual capabilities of social robots. Expectations of social robots are thus of high relevance for the field of Human-Robot Interaction (HRI). While there is recent interest of expectations in the HRI field there is no widely adapted or well formulated evaluation framework that offers a deeper understanding of how these expectations affect the success of the interaction. With basis in social psychology, user experience, and HRI, we have developed an evaluation framework for studying users’ expectations of social robots. We have identified three main factors of expectations for assessing HRI: affect, cognitive processing, and behavior and performance. In our framework, we propose several data collection techniques and specific metrics for assessing these factors. The framework and its procedure enables analysis of the collected data via triangulation to identify problems and insights, which can grant us a richer understanding of the complex facets of expectations, including if the expectations were confirmed or disconfirmed in the interaction. Ultimately, by gaining a richer understanding of how expectations affect HRI, we can narrow the social robot expectation gap and create more successful interactions between humans and social robots in society. 

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 Negative Attitude toward Robots Scale (NARS) is one of the most common questionnaires used in the studies of human-robot interaction (HRI). It was established in 2004, and has since then been used in several domains to measure attitudes, both as main results and as a potential confounding factor. To better understand this important tool of HRI research, we reviewed the HRI literature with a specific focus on practice and reporting related to NARS. We found that the use of NARS is being increasingly reported, and that there is a large variation in how NARS is applied. The reporting is, however, often not done in sufficient detail, meaning that NARS results are often difficult to interpret, and comparing between studies or performing meta-analyses are even more difficult. After providing an overview of the current state of NARS in HRI, we conclude with reflections and recommendations on the practices and reporting of NARS.

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.