Effective feedback is vital for improving motor skills, exercise techniques, and motivating physical activity (PA). While haptic feedback and socially assistive robots (SARs) have been individually studied to enhance exercise experiences, their combination is less explored. This study evaluated the effectiveness of haptic feedback via a vest and auditory-visual feedback from a SAR in improving squatting techniques. Ten healthy participants performed squats under three conditions: haptic feedback, robot feedback, and combined feedback. Although no significant differences were found between the two modalities, participants valued both. Haptic feedback provided immediate, intuitive guidance, while the SAR offered motivational and engaging interactions. The combination appeared to have a synergistic effect, enhancing confidence and performance. These findings highlight the potential of integrating haptic and SAR feedback for exercise coaching and support further research to refine interactive systems in physical training.

Despite the potential benefits of assistive robots for aging populations or individuals with special interactive requirements, such as reduced vision or hearing, there is limited research in this area. We present a repeated-measures study that included twenty-nine participants who interacted in two phases, either blindfolded first and then not blindfolded or vice versa. The study measured trust and negative attitudes towards robots (NARS) before and after handover interactions, as well as social robot attributes (RoSAS) and handover times. Results revealed no significant differences in trust levels or social attributes. However, negative attitudes and handover times were significantly lower when participants interacted first blindfolded. These findings highlight the importance of considering reduced vision capabilities in Human-Robot Collaboration and suggest potential strategies for enhancing interaction experiences in this context. Further research could explore additional factors influencing collaborative interactions and inform the development of more inclusive and effective assistive robot technologies.

There has been a recent trend to test robots and intelligent virtual agents as social interaction partners in public domains. Commercial solutions such as Pepper or Cruz are increasingly being tested in scenarios outside the lab. Though at the same, time customer value and business models for social robots in public spaces are scarce, and with the recently halted production of the Pepper, it seems evident that there is no killer application for social robots in public spaces yet. This work wants to break the boundaries between academia and business and give both sides a venue to exchange lessons learned and develop a roadmap on the technical, legal, ethical, and business challenges for deploying social robots.

Current research on the impact of gender appearance in virtual agents and social robots highlights the danger of transmitting and solidifying existing gender stereotypes. To investigate gender-stereotyped interaction at public spaces in dependency of virtual agents gender, we varied the gender of a virtual agent at a metro station. We used an ethnographic study approach, combining a two-day behavior observation with semi-structured interviews with descriptive and qualitative system log analysis of four weeks. Our results show that topics of conversation differ in dependency of the virtual agents gender: the male virtual agent was asked about topics such as brothels, drugs and alcohol and insulted frequently, while the female one was asked for relationship status or about flirting.

Service and social robot in public scenarios will face various tasks in future applications, such as guiding people or admonishing them to provide assistance or convey social norms. Robots in public spaces might also incorporate roles of authority figures who might admonish people (e.g., security or guard robots). However, recent investigations showed that people ignore the admonishment of robots. Thus, in this work, we are looking at the reasons why people might ignore robots based on the Cognitive Dissonance Theory (CDT). We present the results of two consecutive field observations where a robot admonishes participants (i.e., pedestrians in a shopping mall) and requests them to stop using a smartphone while walking, which is considered an unmoral behavior. In the first field observation, we approached 160 participants over four days and conducted semi-structured interviews with 19 of them. Approximately half of the people ignored the robot, and half of them followed the instructions. Our interview results show that people who ignore the robot indeed use trivialization as a cognitive dissonance reduction strategy to justify ignoring the robot. Based on our analysis of the results, we developed a counter-trivialization strategy that anticipates this dissonance reduction strategy. We admonished 167 participants in our second field observation over four days, and our results show that significantly fewer people ignore the instructions of the robot when the robot uses a counter-trivialization strategy.

Most social robot behaviors in human-robot interaction are designed to be polite, but there is little research about how or when a robot could be impolite, and if that may ever be beneficial. We explore the potential benefits and tradeoffs of different politeness levels for human-robot interaction in an exercise context. We designed impolite and polite phrases for a robot exercise trainer and conducted a 24-person experiment where people squat in front of the robot as it uses (im)polite phrases to encourage them. We found participants exercised harder and felt competitive with the impolite robot, while the polite robot was found to be friendly, but sometimes uncompelling and disingenuous. Our work provides evidence that human-robot interaction should continue to aim for more nuanced and complex models of communication.

Exercising is strongly recommended for prevention and treatment of pathologies with high prevalence such as cardiovascular diseases, cancer, and diabetes. The World Health Organization (WHO) states that insufficient physical activity is one of the leading risk factors for death worldwide. The decrease of physical activity in our society is not just an individual problem but it is influenced by a variety of environmental and social factors. Hence, it is important to target this issue from a multi-perspective and interdisciplinary point of view. This full-day workshop will offer a forum for researchers from a variety of backgrounds to discuss the potentials and limitations of using social robots to promote physical activity.

Adaptation for social companions is a crucial requirement for future applications. Personalized interaction seems to be an important factor for long-term commitment to interact with a social robot. We present a study evaluating the feasibility of a dueling bandit learning approach for preference learning (PL) in Human-Robot Interaction (HRI). Furthermore, we explore whether the embodiment of the PL agent has an influence on the user's evaluation of the learner. We conducted a study (n=53) comparing a graphical user interface (GUI), a virtual robot and a real robot. We found no difference regarding the preference for the virtual or real robot. We used the obtained study data to compare the PL approach against a strategy that randomly selects preference rankings. The results show that that the dueling bandit PL approach can be used to learn a user's preference in HRI.

We present a concept for long-term feedback during robot assisted indoor cycling training. Our feedback model captures different aspects from sport motivation theory. Furthermore, we present our designed measurements to evaluate the robot's persuasiveness and user's compliance. We conducted an intensive 18-day isolation study in two campaigns (e.g. socially assistive robot vs. display instructed, n=16) in cooperation with the German Aerospace Center. The results show that users tend to comply to the robot's instructions and that there is a significant difference in compliance between the two conditions.

The topic of motivation is a crucial issue for various human-robot interaction (HRI) scenarios. Interactional aspects of motivation can be studied in human-human interaction (HHI) and build the basis for modeling a robot's interactional conduct. Using an ethnographic approach we explored the factors relevant in the formation of motivation-relevant processes in an indoor-cycling activity. We propose an interactive, action-based motivation model for HRI that has been implemented in an autonomous robot system and tested during a long-term HRI study. The model is based on micro-analyses of human indoor cycling courses and resulted in an adaption of specific dialog patterns for HRI. A qualitative evaluation - accompanied by a quantitative analysis - demonstrated that the transfer of interaction patterns from HHI to HRI was successful with participants benefitting from the interaction experience (e.g., performance, subjective feeling of being motivated).