People's successful coexistence with robots strictly depends on people's acceptance of robots' presence in their daily activities. This is particularly relevant when the robot's actions may interfere with or intrude on people's activities, creating discomfort and possible rejection. We believe that people's acceptance of a robot may vary depending on the activities they are involved in. In this study, we investigate the impact of a robot's actions on people's engagement in an activity while the robot has the task of monitoring them. We observed the behaviours of 18 older adults with respect to the robot while they were carrying out tasks that require different cognitive workloads (e.g., working at the PC, talking on the phone). We used subjective and objective metrics, such as social cues, to evaluate people's engagement in the robot and their disengagement in their own tasks. We observed that people were distracted by the robot's behaviours based on the cognitive loads required by their activity. Our results show that variation in people's engagement in the robot and the task is affected by their perception of the usefulness of and trust in the robot, and by individuals' personality traits and acceptance of the robot. People with higher trust in the robot, and a higher degree of conscientiousness and emotional stability, tend to continue with their task, paying less attention to the robot. We observed, in contrast, that a robot perceived as a social entity caught more easily their attention when people have a higher extroverted personality. Our findings also showed that variations in the affective and emotional demeanour of the participants are a predictor of their distraction to an external observer.

Providing a robot with the capabilities of understanding and effectively adapting its behaviour based on human mental states is a critical challenge in Human–Robot Interaction, since it can significantly improve the quality of interaction between humans and robots. In this work, we investigate whether considering human mental states in the decision-making process of a robot improves the transparency of its behaviours and mitigates potential human’s false beliefs about the environment during collaborative scenarios. We used Bayesian inference within a Hierarchical Reinforcement Learning algorithm to include human desires and beliefs into the decision-making processes of the robot, and to monitor the robot’s decisions. This approach, which we refer to as Hierarchical Bayesian Theory of Mind, represents an upgraded version of the initial Bayesian Theory of Mind, a probabilistic model capable of reasoning about a rational agent’s actions. The model enabled us to track the mental states of a human observer, even when the observer held false beliefs, thereby benefiting the collaboration in a multi-goal task and the interaction with the robot. In addition to a qualitative evaluation, we conducted a between-subjects study (110 participants) to evaluate the robot’s perceived Theory of Mind and its effects on transparency and false beliefs in different settings. Results indicate that a robot which considers human desires and beliefs increases its transparency and reduces misunderstandings. These findings show the importance of endowing Theory of Mind capabilities in robots and demonstrate how these skills can enhance their behaviours, particularly in human–robot collaboration, paving the way for more effective robotic applications.

Deception is a complex phenomenon that is deeply intertwined with human social interactions. Deception between humans often manifests through verbal, vocal, and visible behaviors. While it often has unethical implications, it can also serve valuable social functions, such as maintaining relationships, protecting emotions, or managing difficult situations. For this reason, it is also being investigated in robotic applications. In this work, we propose a framework for implementing specific robot behaviors through non-strategic cues of deception (known as leakage), during deceptive communication with humans. We dwell on the ethical dimensions of robotic deception by acknowledging the implication of possible physical or psychological harm in sensitive contexts, such as healthcare and assistive robotics. We propose to mitigate possible drops in trust towards robots using more transparent and human-like deceptive behaviors, by equipping robots with seemingly unintentional behaviors that betray deception. To this extent, we propose a low-risk educational scenario where a robot interacts with students in a problem-solving game to test deception leaking's effects on students' perceptions of the robot, perceived human-likeness, intentionality, engagement, and trust in the robot.

Patients falling from their beds is still one of the major complications of the hospital’s treatments. To address this issue, this research investigates the use of social robots for the identification of potential bed-related falls in hospitals. Using a robot camera and human pose estimation techniques, the patient’s position in the bed is extracted, and a threshold-based algorithm is used to identify any anomalies that could indicate a fall. Due to the absence of publicly available datasets, a synthetic dataset was created using a simulation environment to develop and tune the detection algorithm. A user study was conducted to validate the proposed approach and evaluate people’s perception of the robot. The system achieved an accuracy of 90.9% in the controlled setting using real data. Participants rated the robot as significantly more trustworthy and behaviorally aware when it detects a possible fall, suggesting that timely and meaningful assistance improved the perceived social competence of the robot. These findings highlight the feasibility of deploying social robots as monitoring systems in sensitive clinical settings, offering a cost-effective and socially acceptable solution.

For social robots to collaborate effectively, they must infer and correct false human beliefs, especially when misconceptions directly impact task outcomes or pose safety risks to humans. In this work, we investigated whether a robot’s ability to detect and rectify users’ false beliefs improves trust and perceived social competence. In an in-person between-subject study, 98 participants collaborated with a robot to solve a task. Participants interacted with either a robot that actively corrected their false beliefs by using Theory of Mind or one that complied with their incorrect instructions. Contrary to expectations, trust, mental state attribution, and perceived warmth or competence did not differ between groups. The results also showed that human reluctance to trust the robot’s input persisted, suggesting that belief correction alone cannot overcome relational barriers. In addition, the study showed that participants who trusted the robot’s corrections perceived it as more socially attuned.

In an interdisciplinary and evolving research field like human-robot interaction, clear and precise results reporting is essential for study comparability and replicability. To address the lack of a standard for such reporting and, at the same time, provide guidance for novices in the field, we have developed a web-based reporting form to capture human-robot interaction studies, serving as a model for how conferences could adopt it into the submission pipeline. In this work, we present a formative evaluation of this form regarding its level of detail, format and clarity, and the perceived benefits for authors, reviewers, and the community as a whole. We report the expert review of nine researchers who highlight the substantial value of this tool. In addition, these experts also provide suggestions for improvements to its form and the addition of details surrounding qualitative reporting.

Food recommender systems have become valuable tools across various domains, including health-oriented applications that provide personalised dietary advice. Recent studies have shown the potential of integrating recommender systems with assistive robots to promote healthy eating habits, especially among older adults. While transformers and Large Language Models showed advanced reasoning capability for effective recommendation systems, they might have limited knowledge and understanding of the users’ personal preference and requirements. This lack of information can negatively affect their effectiveness and user’s satisfaction. We present a novel transformer-assisted, multi-interface recommendation system for generating food recommendations based on user profiles using a custom dataset including dietary and nutritional information. We conducted a user study with 40 participants for evaluating whether a robot is able to persuade users’ in accepting its food recommendation. Our study found that participants responded positively to the interactions with the robot, showing high satisfaction and trust in the recommendations.

Human-robot interaction in cooperative and assistive scenarios requires robotic systems to assess the task state and coherently choose their next move.Moreover, it is also fundamental to correctly recognize how the user's stress and emotional response are changing to offer support appropriately.The robot should be able to adapt to different user reactions, considering the situational context, and displaying empathetic behaviors aiming to support and encourage the users.In this work, we aim to assess the impact of empathetic supporting behaviors on the perception of the robot and the users' performance during a collaborative task, as opposed to assistive strategies focusing only on the task's performance.With this objective in mind, we propose a robotic architecture to assist a user in playing a memory game in real-time using a Furhat robot.We conducted a user study where 60 participants played with the robot to evaluate the effects of the two types of Theory of Mind on the assistive task and their perception of the robot.To this extent, the participants interacted with a robot endowed with either Cognitive or Affective Theory of Mind to respectively allow the robot to understand intentions and beliefs, and to show empathetic behaviors to improve the collaboration.The two conditions resulted in achieving the same results in terms of task performance, but the participants rated the emotionally engaged robot higher in perceived social intelligence.

In this work, we investigate whether the transparency of a robot’s behaviour is improved when human preferences on the actions the robot performs are taken into account during the learning process . For this purpose, a shielding mechanism called Preference Shielding is proposed and included in a reinforcement learning algorithm to account for human preferences. We also use the shielding to decide when to provide explanations of the robot’s actions. We carried out a within-subjects study involving 26 participants to evaluate the robot’s transparency. Results indicate that considering human preferences during learning improves legibility compared with providing only explanations. In addition, combining human preferences and explanations further amplifies transparency. Results also confirm that increased transparency leads to an increase in people’s perception of the robot’s safety, comfort, and reliability. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications when a robot has to learn a task in the presence of or in collaboration with a human.

Background. People commonly use deception to gain advantages for themselves and their significant ones, such as with children, for educational purposes, or for protecting someone else feelings. As robots increasingly are being used in various human-centered environments, experts in robotics and social sciences are trying to adapt similar deceptive techniques to social robots, such as in assistive and service applications. However, robots’ ability to engage in deceptive behaviors presents both potential benefits and significant ethical challenges. In this work, we present a systematic review to synthesize current research on the implementation of deceptive robotic behaviors during Human–Robot Interactions (HRI), and its effects on people. Methods. Adopting a comprehensive and flexible methodological approach, we systematically searched Scopus and Web of Science without restricting the publication date. The review focused on studies that explicitly examined the effects of robotic deception on human participants, covering a broad spectrum of methodologies, populations, and outcomes. Results. A total of 16 studies met the inclusion criteria, showing that robotic deception in HRI leads to diverse emotional, cognitive, and behavioral responses. The findings indicate that robotic deception can have diverse impacts, ranging from eroding trust to enhancing engagement and performance under certain conditions. Conclusions. Our systematic review highlights the importance of careful design and management in robotic systems to harness the benefits of deception while mitigating its negative impacts on trust. We advise that future research should explore conditions under which deception may be beneficial and develop strategies to effectively manage its use in HRI.

The evaluation of groups’ dynamics and engagement levels in Human-Robot Interaction (HRI) is crucial for enabling a robot to adapt its behavior and achieve sustained interaction. To address these tasks, various methods have been explored, but these aspects are often treated in isolation. In this work, we present a unified machine-learning approach that addresses the recognition of social group dynamics in a multiparty setting, using that information as a possible index of engagement from an egocentric perspective. We introduce an interaction grouping classifier that considers the robot as a potential group member. Our approach utilizes neural networks trained on a combination of an existing dataset and a newly created one, specifically labeled for this study. The proposed method leverages egocentric data to detect social groups and employs this information as an index of engagement, as it requires agents to be part of the same interacting group, including the robot. Experimental results demonstrate high accuracy in both engagement recognition and group detection. Additionally, tests on F-formations reveal complexities in scenarios involving the robot, underscoring the challenges in these configurations.

Persuasion is defined as the process of changing people’s attitudes and behaviours and social assistive robots are used to favour such changes through a variety of mechanisms. While human-robot deception is considered to present philosophical and psychological issues, it still may have positive consequences. In particular, prosocial deception can be beneficial and have positive influences, such as increasing trust. This work presents an exploration of robot deception and its effects on people’s changes in behaviour and trust in a social assistive robot. In particular, we explore the effects of different types of deception states (superficial, external, and hidden states) on people’s compliance with a social robot during an assistive game scenario. We collected the responses of 63 participants to evaluate their perception of trust in the robot, and their perception of the robot’s deceiving behaviours. Our results showed that the deceiving behaviours of the robot affected people’s trust and that superficial state deception has higher negative effects on people’s perception of the robot and trust in it compared to the other two deceptive states.

The lack of transparency in robotic learning processes poses a significant challenge to effective human-robot collaboration. This is particularly relevant in non-industrial settings because it prevents humans from adequately comprehending a robot’s intentions, progress, and decision-making rationale, which is essential for seamless interaction. To address this issue, this work presents a study where users observe a robot endowed with three distinct emotional/behavioural mechanisms for conveying transparent information about its learning process. The proposed mechanisms use inner speech, emotions, and a combination of the two communication styles (hybrid). To assess and evaluate the transparency of these behavioural models, a between-subject study was conducted with 108 participants. Results indicate that the people’s perception of the robot’s warmth dimension increased when it utilized a hybrid model to explain its learning state. Additionally, increased transparency was observed when the robot used inner speech during the learning process.

This work studies the impact of a robot’s appearance on how people judge robots’ trustworthiness in a public space scenario. An online experimental study was conducted to investigate the effect of the robot’s appearance on the perception of its role and on participants’ willingness to comply with the robot’s request to share sensitive information. The context of the interaction and the robot’s role was presented to the participants using a pre-recorded video filmed from a first-person perspective, encountering and interacting with a Pepper robot at a foreign University. We recruited 54 participants of different ages and nationalities. Each participant was tested with one of the three conditions in which the robot played the same role (which was not explicitly conveyed to the participants) but with a different appearance. Qualitative and quantitative measures were used to collect participants’ responses to evaluate their trust perception in showing their ID documents to the robot and letting the robot take a picture of them. Results showed that the context of interaction played a big part in helping the participants infer the robot’s role and the judgment of sensitivity of the information. Our findings provide insights and a better understanding of which are the factors affecting the perception of trustworthiness of robot for a privacy-sensitive human-robot interaction (HRI).

In the past few years, human-robot deception has been receiving growing attention in several fields (e.g., human-robot interaction, laws, philosophy, and psychology). While deception both in human-human and human-robot interactions may have positive consequences, it still presents philosophical and psychological controversy. In particular, verbal deceptions (i.e., in the form of lies or misleading information) may be judged as intentional behaviour at times. While intentionality has been recognised as fundamental in the development of trust, it is not yet fully clear which mechanisms can be designed to foster trust and the potential issues connected to deception. To this extent, in this study, we investigate whether the ability of mentalizing may be one of such mechanisms. We conducted a user study during a public fair, where participants played an assistive game with a robot endowed with Theory of Mind (ToM). We collected the responses from 37 participants to evaluate their perception of trust in the robot. During the game, the robot may occasionally have deceptive behaviours suggesting the wrong move to the human players. Our results showed that a deceptive robot was less trusted compared to a non-deceiving one. We also found that people’s perception of the robot was positively affected by the frequency of exposure to deception (i.e., wrong suggestions).

People's acceptance and trust in robots are a direct consequence of people's ability to infer and predict the robot's behavior. However, there is no clear consensus on how the legibility of a robot's behavior and explanations should be assessed. In this work, the construct of the Theory of Mind (i.e., the ability to attribute mental states to others) is taken into account and a computerized version of the theory of mind picture sequencing task is presented. Our tool, called the human–robot interaction (HRI) video sequencing task (HRIVST), evaluates the legibility of a robot's behavior toward humans by asking them to order short videos to form a logical sequence of the robot's actions. To validate the proposed metrics, we recruited a sample of 86 healthy subjects. Results showed that the HRIVST has good psychometric properties and is a valuable tool for assessing the legibility of robot behaviors. We also evaluated the effects of symbolic explanations, the presence of a person during the interaction, and the humanoid appearance. Results showed that the interaction condition had no effect on the legibility of the robot's behavior. In contrast, the combination of humanoid robots and explanations seems to result in a better performance of the task.

This work investigated how robots with different service style in food industries are perceived by customers. We analysed responses from 53 participants who evaluated the video interaction of a robot waiter while taking orders and that delivered a customer service experience based on different styles of a service robot interaction (service-focused vs. social-focused). Results indicated that social-focused robots are perceived to have more natural and pleasant behaviours compared with service-focused robots, and therefore better accepted by customers. Social-focused robots were also perceived as having a more persuasive effect on the users' choices. In contrast, service-focused robots were perceived as hasty, and participants did not feel in charge of the food choices to order.

The aim of this workshop is to foster the exchange of insights on past and ongoing research towards effective and long-lasting collaborations between humans and robots. This workshop will provide a forum for representatives from academia and industry communities to analyse the different aspects of HRI that impact on its success. We particularly focus on AI techniques required to implement autonomous and proactive interactions, on the factors that enhance, undermine, or recover humans' acceptance and trust in robots, and on the potential ethical and legal concerns related to the deployment of such robots in human-centred environments. Website: https://sites.google.com/view/trains-hri-2022.

People and robots may need to cross each other in narrow spaces when they are sharing environments. It is expected that autonomous robots will behave in these contexts safely but also show social behaviors. Thereby, developing an acceptable behavior for autonomous robots in the area mentioned above is a foreseeable problem for the Human-Robot Interaction (HRI) field. Our current work focuses on integrating legible non-verbal behaviors into the robot's social navigation to make nearby humans aware of its intended trajectory. Results from a within-subjects study involving 33 participants show that deictic gestures as navigational cues for humanoid robots result in fewer navigation conflicts than the use of a simulated gaze. Additionally, an increase in the perceived anthropomorphism is found when the robot uses the deictic gesture as a cue. These findings show the importance of social behaviors for people avoidance and suggest a paradigm of such behaviors in future humanoid robotic applications.

When navigating in a shared environment, the extent to which robots are able to effectively use signals for coordinating with human behaviors can ameliorate dissatisfaction and increase acceptance. In this paper, we present an online video study to investigate whether familiar acoustic signals can improve the legibility of a robot’s navigation behavior. We collected the responses of 120 participants to evaluate their perceptions of a robot that communicates with one of the three used non-verbal navigational cues (an acoustic signal, an acoustic in pair with a visual signal, and a dissimilar frequency acoustic signal). Our results showed a significant legibility improvement when the robot used both light and acoustic signals to communicate its intentions compared to using only the same acoustic sound. Additionally, our findings highlighted that people also perceived differently the robot’s intentions when they were expressed through two frequencies of the mere sound. The results of this work suggest a paradigm that can help the development of mobile service robots in public spaces.

Expressiveness is a critical feature for the communication between humans and robots, and it helps humans to better understand and accept a robot. Emotions can be expressed through a variety of modalities: kinesthetic (via facial expression), body posture and gestures, auditory, thus the acoustic features of speech, and semantic, thus the content of what is said. One of the most effective modalities to communicate emotions is through facial expressions. Social robots often show facial expressions with coded animations. However, the robot must be able to express appropriate emotional responses according to the interaction with people. In this work, we consider verbal interactions between humans and robots and propose a system composed of two modules for the generation of facial emotions by recognising the arousal and valence values of a written sentence. The first module, based on Bidirectional Encoder Representations from Transformers, is deployed for emotion recognition in a sentence. The second, an Auxiliary Classifier Generative Adversarial Network, is proposed for the generation of facial movements for expressing the recognised emotion in terms of valence and arousal.

Educational robotics is a field aiming at investigating the use of robots in schools to support teaching and learning activities. While several robotic solutions exist in support of the STEM teaching activities, in this work, we present "Classmate Robot" as a new social robot to be used in the classrooms as a support to the learning experience through interaction. Classmate Robot has been designed and developed to improve the effectiveness of the activities by providing a framework where the robot’s behaviors can be personalized, and learning applications can be easily integrated on top of the robot interaction capabilities. This approach aims to increase the engagement of learners. We introduce the ROS-based architecture developed that is divided into three main layers plus an application layer. As a social robot, it combines several multimodal social cues to interact and communicate with students and teachers. Moreover, the robot is endowed with a set of behaviors designed to be compliant with its role of "classmate" in the interaction with the students.

The success of human-robot interaction is strongly affected by the people’s ability to infer others’ intentions and behaviours, and the level of people’s trust that others will abide by their same principles and social conventions to achieve a common goal. The ability of understanding and reasoning about other agents’ mental states is known as Theory of Mind (ToM). ToM and trust, therefore, are key factors in the positive outcome of human-robot interaction. We believe that a robot endowed with a ToM is able to gain people’s trust, even when this may occasionally make errors.In this work, we present a user study in the field in which participants (N=123) interacted with a robot that may or may not have a ToM, and may or may not exhibit erroneous behaviour. Our findings indicate that a robot with ToM is perceived as more reliable, and they trusted it more than a robot without a ToM even when the robot made errors. Finally, ToM results to be a key driver for tuning people’s trust in the robot even when the initial condition of the interaction changed (i.e., loss and regain of trust in a longer relationship).

As people are getting more used to interact with social robots, their expectations of these robots also increase. However, robots are not always able to meet such expectations due to the limitations of the hardware and the software, or it might be possible that robots are simply unable to correctly elaborate the information about the agents, environment and context, and as consequence they produce erroneous behaviours. For example, a robot might get incongruous responses of people from the observations of multimodal systems. In such case, a technique used by humans is verbal irony, or sarcasm which it is a form of verbal irony, to recover from the situation. To this extent, we present a two parts study where we aimed to endow a robot with sarcasm. Results showed that social interacting behaviours, such as paying attention during a conversation, being transparent on the process of thinking and elaborating a response, allow people’s to perceive a robot with higher anthropomorphism and animacy. Moreover, robot failure recovery mechanisms are easier recognised by people when they use verbal incongruence.

Guaranteeing people’s engagement during an interaction is very important to elicit positive and effective emotions in public service scenarios. A robot should be able to detect its interlocutor’s level and mode of engagement to accordingly modulate its behaviours. However, there is not a generally accepted model to annotate and classify engagement during an interaction. In this work, we consider engagement as a multidimensional construct with three relevant dimensions: affective, cognitive and behavioural. To be automatically evaluated by a robot, such a complex construct requires the selection of the proper interaction features among a large set of possibilities. Moreover, manually collecting and annotating large datasets of real interactions are extremely time-consuming and costly. In this study, we collected the recordings of human-robot interactions in a bartending scenario, and we compared different feature selection and regression models to find the features that characterise a user’s engagement in the interaction, and the model that can efficiently detect them. Results showed that the characterisation of each dimension separately in terms of features and regression obtains better results with respect to a model that directly combines the three dimensions.

As we expect that the presence of autonomous robots in our everyday life will increase, we must consider that people will have not only to accept robots to be a fundamental part of their lives, but they will also have to trust them to reliably and securely engage them in collaborative tasks. Several studies showed that robots are more comfortable interacting with robots that respect social conventions. However, it is still not clear if a robot that expresses social conventions will gain more favourably people's trust. In this study, we aimed to assess whether the use of social behaviours and natural communications can affect humans' sense of trust and companionship towards the robots. We conducted a between-subjects study where participants' trust was tested in three scenarios with increasing trust criticality (low, medium, high) in which they interacted either with a social or a non-social robot. Our findings showed that participants trusted equally a social and non-social robot in the low and medium consequences scenario. On the contrary, we observed that participants' choices of trusting the robot in a higher sensitive task was affected more by a robot that expressed social cues with a consequent decrease of their trust in the robot.

In this work we investigate how humans' awareness of a social robot's capabilities affect their trust in the robot. We present a user study that relates knowledge on different quality levels to participants' ratings of trust. Primary school pupils were asked to rate their trust in the robot after three types of interactions: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is not significantly affected across different domains after each session. It did not appear to be significant differences in trust tendencies for the different experiences either; however, our results suggest that human users trust a robot more the more awareness about the robot they have.

This work investigates how human awareness about a social robot's capabilities is related to trusting this robot to handle different tasks. We present a user study that relates knowledge on different quality levels to participant's ratings of trust. Secondary school pupils were asked to rate their trust in the robot after three types of exposures: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is positively affected across different domains after each session, indicating that human users trust a robot more the more awareness about the robot they have.

The aim of the proposed investigation is to provide the users with the capability of creating robot teams “on- the-fly” using grouping strategies expressed through speech. Our working hypothesis is that people are inclined to assemble objects into macro-entities, or groups, according to perceptual principles. We observed the real linguistic utterances used by individuals in a testing environment, showing that the type of robots and their mutual arrangements can affect both the choice of elements to form a team, and the way such choice is made. Moreover, we provide an initial insight for the capabilities needed by a robot for reasoning about its membership in a team.