Robots are increasingly joining human teams, where collaboration and competition often coexist. However, their impact on human risk-taking remains unclear. We investigated whether collaborating with or competing against a humanoid robot influences risk-taking behavior and performance using the Balloon Analogue Risk Task (BART). In this task, participants pump a virtual balloon to earn points, but risk losing all their points if the balloon bursts. They interacted with a NAO robot in a pre-registered, mixed-design study (N=43), with interaction type (collaboration vs. competition) as a between-subjects factor and team context (solo vs. social) as a within-subjects factor. We found that social interaction increased risk behavior compared to playing alone. Competition fostered strategic risk-taking, with more pumps, fewer bursts and higher scores resulting from optimal strategy adoption. In contrast, collaboration fostered exploratory risk-taking with increased risk but without performance gains. Additional exploratory analysis revealed that men took more risks in collaboration. Our findings demonstrate that the type of social interaction, rather than the mere presence of a robot, shapes how humans take risks with robots.

Social navigation has become increasingly important for robots operating in human environments, yet many newly proposed navigation methods remain narrowly tailored or exist only as proof-of-concept prototypes. Building on our previous work with Arena, a social navigation development platform, we now propose, Arena-Bench 2.0 a comprehensive social navigation benchmark of state-of-the-art planners, fully integrated into the Arena framework. To achieve this, we developed a novel plugin structure—implemented on ROS2—to streamline the integration process and ensure straightforward, efficient workflows. As a demonstration, we integrated various learning-based and model-based navigation approaches and constructed a diverse set of social navigation scenarios to rigorously evaluate each planner. Specifically, we introduce a scenario generation node that allows users to construct complex, realistic social contexts through a web-based interface. We subsequently perform an extensive benchmark of all integrated planners, assessing both navigational and social metrics. Our evaluation also considers factors such as sensor input, reaction time, and latency, enabling insights into which planner may be most appropriate under different circumstances. The findings offer valuable guidance for selecting suitable planners for specific scenarios. The code is publicly available at https://github.com/Arena-Rosnav.

Beyond conscious beliefs and goals, automatic cog-nitive processes shape our social encounters, and interactions with complex machines like social robots are no exception. With this in mind, it is surprising that research in human-robot interaction (HRI) almost exclusively uses explicit measures, such as subjective ratings and questionnaires, to assess human attitudes towards robots - seemingly ignoring the importance of implicit measures. This is particularly true for research focusing on the question whether or not humans are willing to attribute complex mental states (i.e., mind perception), such as agency (i.e., the capacity to plan and act) and experience (i.e., the capacity to sense and feel), to robotic agents. In the current study, we (i) created the mind perception implicit association test (MP-IAT) to examine subconscious attributions of mental capacities to agents of different degrees of human-likeness (here: human vs. humanoid robot), and (ii) compared the outcomes of the MP-IAT to explicit mind perception ratings of the same agents. Results indicate that (i) already at the subconscious level, robots are associated with lower levels of agency and experience compared to humans, and that (ii) implicit and explicit measures of mind perception are not significantly correlated. This suggests that mind perception (i) has an implicit component that can be measured using implicit tests like the IAT and (ii) might be difficult to modulate via design or experimental procedures due to its fast-acting, automatic nature.

If robots could engage face-processing they would increase the likelihood they are accepted as social companions. However, research has not examined whether and when robot “faces” engage face-processing. The current study examined whether facial-width-to-height ratio (FWHR) modulated face-processing with robots using the “inversion task”-a commonly utilized measure of face perception that leverages the finding that inverting face stimuli hurts recognition performance (i.e., inversion effects) compared to other types of stimuli. We predicted that recognition performance would be more effected by inversion when robots had a low rather than high FWHR. While our statistical results were not significant, descriptive results trended in favor of our hypothesis, demonstrating robots with a lower FHWR had larger inversion effects than robot with a higher FWHR. While more research will be needed to clarify these results, the inversion task is a potentially useful tool to measure the social acceptance of robots through the detection of facial processing.