Increasing encounters between people and autonomous service robots may lead to conflicts due to mismatches between human expectations and robot behaviour. This interactive online study (N = 335) investigated human-robot interactions at an elevator, focusing on the effect of communication and behavioural expectations on participants’ acceptance and compliance. Participants evaluated a humanoid delivery robot primed as either submissive or assertive. The robot either matched or violated these expectations by using a command or appeal to ask for priority and then entering either first or waiting for the next ride. The results highlight that robots are less accepted if they violate expectations by entering first or using a command. Interactions were more effective if participants expected an assertive robot which then asked politely for priority and entered first. The findings emphasize the importance of power expectations in human-robot conflicts for the robot’s evaluation and effectiveness in everyday situations.

People’s ascription of intentional agency to robots necessitates understanding how, when, and why people attribute robot behavior to underlying intentional states. While many studies explored mind attribution to robots including its determinants and consequences, little attention has been given to the attribution of second-order mental states, such as a robot’s beliefs about people’s intentions during interactions. In an online study (n = 155), participants watched a video of a humanoid robot tracking a ball hidden under one of two cups. 19% of participants predicted that the robot could correctly locate the ball after it was displaced twice by a person deliberately distracting the robot, indicating implicit attribution of second-order beliefs to the robot. These implicit attributions influenced participants’ actions in a subsequent interactive game with a virtual counterpart of the robot but did not affect their explicit assessments of the robot’s second-order reasoning. In contrast, observing the robot demonstrate second-order reasoning by correctly identifying the ball’s location affected participants’ explicit attributions but not their behavior in the interactive game. This reveals a complex interplay between implicit and explicit attribution processes in how people interpret robot behavior.

The rapid advancement of autonomous vehicle (AV) technology has opened up new possibilities and challenges in the domain of human–agent interaction. As AVs become increasingly prevalent on our roads, it is crucial to understand how humans perceive and interact with these intelligent systems. This workshop aims to bring together researchers and practitioners to explore the perception of cars as social agents. We explore the shift in user perception and the implications for interactions between autonomous vehicles, human drivers, and vulnerable road users (pedestrians, cyclists, etc.). Additionally, we investigate the communication of goals and intentions between cars and humans, as well as issues related to mixed agency, stakeholder perspectives, in-vehicle avatars, and human–vehicle power dynamics. The workshop aims to uncover the benefits, risks, and design principles associated with this emerging paradigm.

As automated shuttle buses gradually become a part of urban traffic solutions, their interactions with vulnerable road users need careful consideration. However, the cyclists’ perspectives on autonomous shuttle buses have not been explored extensively. This research addresses this gap by surveying 50 cyclists who regularly encounter automated shuttle buses. The results show that, in general, cyclists exhibit a high level of trust in the safety of the buses. Nevertheless, approximately one-third of the cyclists expressed disapproval as the buses tend to drive on the bicycle lane, leading them to wish for infrastructural solutions to avoid forcing cyclists to divert to pedestrian walkways. Identifying potential conflicts like these is vital for the development of effective and acceptable human-agent interactions in road traffic environments.

Understanding how people attribute behavior to underlying mental states is crucial to the study of human social interactions. Previous research has established that people rely on attributing mental states to interpret and interact with artificial agents. For example, a pedestrian encountering a driverless vehicle at a crosswalk might view the vehicle as knowing (or not) that there is a pedestrian in front of it. Nevertheless, little attention has been devoted to investigating people’s attributions of higher-order mental states, i.e., mental states that are about the mental states of others (e.g., the vehicle’s beliefs about the pedestrian’s intentions). Addressing this research gap, the present study conducted a survey to explore people’s attributions and preferences concerning higher-order mental states in three types of artificial agents (AI chatbot, virtual assistant, and self-driving car), alongside two human agents (participants themselves, referred to as you, and five-year-old child).

The topic of mental state attribution to robots has been approached by researchers from a variety of disciplines, including psychology, neuroscience, computer science, and philosophy. As a consequence, the empirical studies that have been conducted so far exhibit considerable diversity in terms of how the phenomenon is described and how it is approached from a theoretical and methodological standpoint. This literature review addresses the need for a shared scientific understanding of mental state attribution to robots by systematically and comprehensively collating conceptions, methods, and findings from 155 empirical studies across multiple disciplines. The findings of the review include that: (1) the terminology used to describe mental state attribution to robots is diverse but largely homogenous in usage; (2) the tendency to attribute mental states to robots is determined by factors such as the age and motivation of the human as well as the behavior, appearance, and identity of the robot; (3) there is a computer < robot < human pattern in the tendency to attribute mental states that appears to be moderated by the presence of socially interactive behavior; (4) there are conflicting findings in the empirical literature that stem from different sources of evidence, including self-report and non-verbal behavioral or neurological data. The review contributes toward more cumulative research on the topic and opens up for a transdisciplinary discussion about the nature of the phenomenon and what types of research methods are appropriate for investigation.

The explainability of robotic systems depends on people’s ability to reliably attribute perceptual beliefs to robots, i.e., what robots know (or believe) about objects and events in the world based on their perception. However, the perceptual systems of robots are not necessarily well understood by the majority of people interacting with them. In this article, we explain why this is a significant, difficult, and unique problem in social robotics. The inability to judge what a robot knows (and does not know) about the physical environment it shares with people gives rise to a host of communicative and interactive issues, including difficulties to communicate about objects or adapt to events in the environment. The challenge faced by social robotics researchers or designers who want to facilitate appropriate attributions of perceptual beliefs to robots is to shape human–robot interactions so that people understand what robots know about objects and events in the environment. To meet this challenge, we argue, it is necessary to advance our knowledge of when and why people form incorrect or inadequate mental models of robots’ perceptual and cognitive mechanisms. We outline a general approach to studying this empirically and discuss potential solutions to the problem.

The reported study investigated three gender-related effects on the rated persuasiveness of a speech given by a humanoid robot: (1) the female or male gendered voice and visual appearance of the robot, (2) the female or male gender of the participant, and (3) the interaction between robot gender and participant gender. The study employed a measure of persuasiveness based on the Aristotelian modes of persuasion: ethos, pathos and logos. In contrast to previous studies on gender bias toward intelligent virtual agents and robots, the gender of the robot did not influence the rated persuasiveness of the speech, and female participants rated the speech as more persuasive than men overall.

Several Wizard-of-Oz techniques have been developed to make robots appear autonomous and more social in human-robot interaction. Many of the existing solutions use control interfaces that introduce significant time delays and hamper the robot operator's ability to produce socially appropriate responses in real time interactions. We present work in progress on a novel wizard control interface designed to overcome these limitations:a motion tracking-based system which allows the wizard to act as if he or she is the robot. The wizard sees the other through the robot's perspective, and uses his or her own bodily movements to control it. We discuss potential applications and extensions of this system, and conclude by discussing possible methodological advantages and disadvantages.

In an explorative study concerning the social acceptance of two specific humanoid robots, the experimenter asked participants (N = 36) to place a book in an adjacent room. Upon entering the room, participants were confronted by a NAO or a Pepper robot expressing persistent opposition against the idea of placing the book in the room. On average, 72% of participants facing NAO complied with the robot's requests and returned the book to the experimenter. The corresponding figure for the Pepper robot was 50%, which shows that the two robot morphologies had a different effect on participants' social behavior. Furthermore, results from a post-study questionnaire (GODSPEED) indicated that participants perceived NAO as more likable, intelligent, safe and lifelike than Pepper. Moreover, participants used significantly more positive words and fewer negative words to describe NAO than Pepper in an open-ended interview. There was no statistically significant difference between conditions in participants' negative attitudes toward robots in general, as assessed using the NARS questionnaire.