Georgios Angelopoulos

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.

As robots become increasingly integrated into our daily lives, the need to make them transparent has never been more critical. Yet, despite its importance in human-robot interaction, a standardized measure of robot transparency has been missing until now. This paper addresses this gap by presenting the first comprehensive scale to measure perceived transparency in robotic systems, available in English, German, and Italian languages. Our approach conceptualizes transparency as a multidimensional construct, encompassing explainability, legibility, predictability, and meta-understanding. The proposed scale was a product of a rigorous three-stage process involving 1,223 participants. Firstly, we generated the items of our scale, secondly, we conducted an exploratory factor analysis, and thirdly, a confirmatory factor analysis served to validate the factor structure of the newly developed TOROS scale. The final scale encompasses 26 items and comprises three factors: Illegibility, Explainability, and Predictability. TOROS demonstrates high cross-linguistic reliability, inter-factor correlation, model fit, internal consistency, and convergent validity across the three cross-national samples. This empirically validated tool enables the assessment of robot transparency and contributes to the theoretical understanding of this complex construct. By offering a standardized measure, we facilitate consistent and comparable research in human-robot interaction in which TOROS can serve as a benchmark.

The emergence of autonomous vehicles has raised ethical considerations regarding their controlling software. The focus is on defining ethical settings that determine the response to moral dilemmas, akin to the Trolley Problem. The study explores the interaction with a robot that engages users in a dialogue about such ethical dilemmas, allowing users to align vehicles' behaviour with their ethical preferences. Additionally, providers of these vehicles can have a codified version of user preferences to address potential real-world issues, for instance, by adjusting insurance premiums. The research details designing and implementing a human-robot interaction system for eliciting ethical settings in autonomous vehicles.

The adoption of Reinforcement Learning (RL) in several human-centred applications provides robots with autonomous decision-making capabilities and adaptability based on the observations of the operating environment. In such scenarios, however, the learning process can make robots' behaviours unclear and unpredictable to humans, thus preventing a smooth and effective Human-Robot Interaction (HRI). As a consequence, it becomes crucial to avoid robots performing actions that are unclear to the user. In this work, we investigate whether including human preferences in RL (concerning the actions the robot performs during learning) improves the transparency of a robot's behaviours. For this purpose, a shielding mechanism is included in the RL algorithm to include human preferences and to monitor the learning agent's decisions. We carried out a within-subjects study involving 26 participants to evaluate the robot's transparency in terms of Legibility, Predictability, and Expectability in different settings. Results indicate that considering human preferences during learning improves Legibility with respect to providing only Explanations, and combining human preferences with explanations elucidating the rationale behind the robot's decisions further amplifies transparency. Results also confirm that an increase in transparency leads to an increase in the safety, comfort, and reliability of the robot. These findings show the importance of transparency during learning and suggest a paradigm for robotic applications with human in the loop.

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.

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.