Robots are no longer just tools—they are becoming social agents that can shape how we engage with culture. This study examines what influences visitors’ perceptions of an autonomous museum guide robot, focusing not only on technical capabilities but also on human-centered factors. In a maritime exhibition, 34 participants interacted with a fully autonomous, LLM-powered robot acting as a museum guide. Using self-report questionnaires, we explored how individual differences - age and prior experience with robots — interacted with experimental conditions to shape participants’ impressions of the robot. Our findings suggest that these personal factors significantly affect how visitors evaluate the robot, suggesting that effective design must reflect the diversity of users’ experiences and expectations. By acknowledging the complexity of human-robot interaction, we move closer to creating robotic guides that are not only functional but also socially attuned.

The "Robots for Humans" (RfH) workshop bridges the Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI) communities. The workshop encourages methodological exchange and explores theoretical, technical and design solutions in robotics. Join us to explore the intricate bond between humans and robots for thoughtful HRI advancements.

In this paper, we investigated how shared experience-based counterfactual explanations affected people's performance and robots' persuasiveness during a decision-making task in a social HRI context. We used the Connect 4 game as a complex decision-making task where participants and the robot had to play as a team against the computer. We compared two strategies of explanation generation (classical vs shared experience-based) and investigated their differences in terms of team performance, the robot's persuasive power, and participants' perception of the robot and self. Our results showed that the two explanation strategies led to comparable performances. Moreover, shared experience-based explanations - based on the team's previous games - gave higher persuasiveness to the robot's suggestions than classical ones. Finally, we noted that low-performers tend to follow the robot more than high-performers, providing insights into the potential danger for non-expert users interacting with expert explainable robots.

Social robots are expected to become more and more used in the education field. However, in the interaction between children and social robots, how robots are perceived in social contexts is still under investigation. In this exploratory study, we aimed to investigate how children’s expectations and demographical characteristics (N= 53, 9-14 years old) influence their perception of robot NAO during an education training program in schools. MANCOVA analysis conducted over questionnaire data indicates a positive correlation between the acceptance of the robot and the enjoyment of interacting with it. We found evidence that the more students accepted the robot, the more they perceived the group environment positively. Through a Correspondence Analysis, we investigate which are the preferred features of a robot according to the age of participants. The study suggests that a better opinion of robotics is a factor that can improve the learning environment in this specific context. Our exploratory study encourages conducting studies in-the-wild using self-reported measures to understand the implication of Child-Robot Interaction better.

In this work, we performed a user study in which participants had to solve a human-robot teaming decision-making task (the Connect 4 game) with an explainable vs non-explainable robot. During the task, the robot provided suggestions and, depending on the experimental condition, explanations to justify those suggestions. We compared participants’ behaviours in interacting with both types of robots. In particular, we investigated how participants’ personality dimensions and previous experiences with the iCub robot impacted participants’ decision-making. We also studied how participants aligned with iCub’s playing style as the interaction continued. Our results show that participants’ negative agency and agreeableness substantially impacted how they accepted the robot’s suggestions when it provided example-based counterfactual explanations. We also observed a learning effect: participants tended to align with the robot’s playing style during the interaction. However, the participants’ learning depended not only on the presence of the explanations, but also on the time spent with the robot. Moreover, the human-robot team’s victories were mainly attributable to the robot’s persuasiveness rather than the participants’ skills in the game.