Robot Personality Research Articles

Bridging the gap: Exploring the nexus of service robot personalization, customer identification, and citizenship behaviors

This study explores the multifaceted impact of service robot personalization on customer citizenship behavior, integrating cognitive and affective identification as serial mediators. Leveraging social identity theory, this study examines the intricate psychological processes underlying human-robot interactions. Additionally, the study introduces customer technology readiness as a moderator, acknowledging individual differences in responses to personalization. Results indicate that personalization positively influences alignment perception and identification, subsequently fostering customer citizenship behavior. Furthermore, technology readiness enhances these relationships, emphasizing the need for tailored strategies based on customers' technological predispositions. This research contributes a nuanced understanding of service robot interactions, guiding businesses in strategically deploying personalized interventions to align with diverse customer expectations and preferences.

Gaze modeling in multi-party dialogues and extraversion expression through gaze aversion control

Gaze control plays a crucial role in the context of conversational robots involving multiple participants. Particularly, gaze aversion is not only important for improving human likeness but is also strongly associated with the expression of a robot's personality and should be accounted for dialogue interactions. While previous studies on gaze models considering multi-party conversations have been conducted, those considering gaze aversion are limited. In this study, we developed a gaze motion generation model, focusing on three features: (1) the gaze target (towards dialogue partners or gaze aversion), (2) the gaze duration, and (3) the eyeball direction during gaze aversion. We extracted the gaze model parameters of two individuals with distinct extraversion from a multimodal three-party dialogue database and created a model that can modulate extraversion expression by setting a gaze aversion ratio parameter. We implemented this model on an android robot, which can reproduce human-like gaze behaviors. Video-based experiments have been conducted where participants watched videos of the robot talking with two humans, replicating the three-party dialogue scenarios used in this study, and evaluated the impressions on the robot's extraversion. The results demonstrated that by controlling gaze aversion parameters, the robot could exhibit various levels of extraversion.

How Service Robots Facilitate User Self-Disclosure: The Roles of Personality, Animacy, and Automated Social Presence

This study investigates whether an extroverted personality of service robots can facilitate user self-disclosure and enhance human–robot intimacy. An experiment of 2 2 between-subjects factorial design was conducted to examine the effects of robot personality (extroverted and introverted) and human personality (extroverted and introverted) on user self-disclosure and intimacy. Sixty-four participants from a university campus were included in this study. We simulated a financial investment scenario in which the participants were required to disclose personal information to a robot acting as an investment advisor. Functional near-infrared spectroscopy (fNIRS) was used to capture participants’ prefrontal cortex (PFC) activity. The results revealed a positive effect of robot extroversion on user self-disclosure and intimacy, and this impact was stronger among extroverted participants than among introverted ones. Additionally, perceived robot extroversion enabled users to regard the robot as more animate, promoting automated social presence and ultimately enhancing self-disclosure and intimacy. According to the fNIRS results, the participants who interacted with an extroverted robot had stronger activation in the medial PFC than those who interacted with an introverted robot. The self-reported and fNIRS results were consistently aligned, demonstrating the positive role of extroversion in service robots. These findings provide theoretical and practical insights into the design of service robots in contexts requiring user disclosure.

Towards an Integrative Framework for Robot Personality Research

Within human-robot interaction (HRI), research on robot personality has largely drawn on trait theories and models, such as the Big Five and OCEAN. We argue that reliance on trait models in HRI has led to a limited understanding of robot personality as a question of stable traits that can be designed into a robot plus how humans with certain traits respond to particular robots. However, trait-based approaches exist alongside other ways of understanding personality, including approaches focusing on more dynamic constructs such as adaptations and narratives. We suggest that a deep understanding of robot personality is only possible through a cross-disciplinary effort to integrate these different approaches. We propose an Integrative Framework for Robot Personality Research (IF), wherein robot personality is defined not as a property of the robot, nor of the human perceiving the robot, but as a complex assemblage of components at the intersection of robot design and human factors. With the IF, we aim to establish a common theoretical grounding for robot personality research that incorporates personality constructs beyond traits and treats these constructs as complementary and fundamentally interdependent.

Fusion of Time-of-Flight Based Sensors with Monocular Cameras for a Robotic Person Follower

Human-robot collaboration (HRC) is becoming increasingly important in advanced production systems, such as those used in industries and agriculture. This type of collaboration can contribute to productivity increase by reducing physical strain on humans, which can lead to reduced injuries and improved morale. One crucial aspect of HRC is the ability of the robot to follow a specific human operator safely. To address this challenge, a novel methodology is proposed that employs monocular vision and ultra-wideband (UWB) transceivers to determine the relative position of a human target with respect to the robot. UWB transceivers are capable of tracking humans with UWB transceivers but exhibit a significant angular error. To reduce this error, monocular cameras with Deep Learning object detection are used to detect humans. The reduction in angular error is achieved through sensor fusion, combining the outputs of both sensors using a histogram-based filter. This filter projects and intersects the measurements from both sources onto a 2D grid. By combining UWB and monocular vision, a remarkable 66.67% reduction in angular error compared to UWB localization alone is achieved. This approach demonstrates an average processing time of 0.0183s and an average localization error of 0.14 meters when tracking a person walking at an average speed of 0.21 m/s. This novel algorithm holds promise for enabling efficient and safe human-robot collaboration, providing a valuable contribution to the field of robotics.

Legal Personality of Robots

Developing a well-defined legal framework for robots is vital in this age of rapid advancement in technology. Such regulation is important not only to protect patient rights, but also to encourage responsible technological innovation and maximize the potential of robotics. In my research on the legal personality for robots, given the novelty of the topic and scarcity of specialized references, the research was conducted through extrapolating and analyzing related systems and regulations in the field of technology, in addition to studying relevant academic research on legal personality. A range of perspectives and trends were reviewed regarding granting legal personality to robots. The research began with the topic of legal personality in general, then discussed the notion of granting legal personality to robots between proponents and opponents. It also examined the position of countries and international organizations on robot legal personality, which have not yet recognized independent robots as legal persons since robots do not possess finances separate from their manufacturing companies, nor full autonomy from their human makers and operators. It was emphasized that granting legal personality to any entity requires a specific criterion which is the capacity to acquire rights and duties, assume obligations, and legal personality for robots is mainly attributed to the principle of appropriateness, legal necessity and their ability to act with a degree of consciousness and independence, which reinforces the likelihood of their committing harmful acts, causing damage to others. Given the treatment of robots in most countries as objects, the European Parliament put forward the human agent theory. Therefore, legislators should intervene to establish a clear legal framework that defines the legal nature of robots in a way that protects patient interests without discouraging companies and inventors from innovating to maximize the benefits of robotics and artificial intelligence technologies.

The Impacts of Social Humanoid Robot’s Nonverbal Communication on Perceived Personality Traits

The use of humanoid robots has surged in recent decades. However, the nonverbal features in shaping robot personalities remain underexplored. This study investigates how nonverbal cues (including textual and gestural elements) can generate a spectrum of robot personality traits (introvert, ambivert, and extrovert) and evaluates their impact on users’ cognitive perceptions. Textual manipulations involved three iterations, adjusting word count, information structure, and visual effects. Gestural designs underwent two iterations, altering movement frequency, speed, and size. Multiple empirical studies were conducted to assess the development of robot personality traits and their effects. The results confirm the effectiveness of these nonverbal approaches in characterizing diverse robot personalities and significantly influencing users’ cognitive framing. This research provides valuable design guidelines for leveraging a humanoid robot’s nonverbal features to create a variety of personality traits. Our findings emphasize the importance of considering a spectrum of robot personalities rather than focusing solely on extreme traits.

Human-Centered Design in Human-Robot Interaction Evaluating User Experience and Usability

Social robotics is a fledgling field with a lot of potential. For several decades, it was a concept defined by research and theoretical approaches. Robots have gradually found their way into various aspects of our society as scientific and technological breakthroughs have allowed them to do, and they are now ready to leave the industry and enter our daily lives. In this regard, user experience is critical to ensuring an effortless and natural connection between robots and people. This study concentrated on the user interface approach in terms of a robot's embodiment, focusing on its motions, gestures, and dialogues. The goal was to examine how robotic platforms and people interact, as well as what differences should be considered while building robot activities. To accomplish this goal, a quantitative and qualitative investigation based on actual conversations between many human beings and the artificially intelligent platform was done. The information was acquired by capturing each interaction and having every participant fill out a questionnaire. The findings revealed that participants enjoyed communicating with a robot and considered it engaging, which resulted in increased trust and pleasure. However, the robot's response delays and faults created dissatisfaction and disconnection. The study discovered that adding embodiment into the robot's design increased its user experience and that the robot's personality and conduct were important elements. It was established that robotic platforms, as well as their look, movements, and mode of communication, had a significant impact on user perception and interaction.

Older adults’ user experience with introvert and extravert humanoid robot personalities

AbstractHumanoid robots can be an effective tool for the cognitive training of older adults. For this purpose, it is important that their interaction be engaging. In this study, we investigate whether proposing robots exhibiting extraverted or introverted personalities can improve user experience. In particular, we have designed and implemented a set of multi-modal cues for such personality traits, which have been exploited in an application proposing typical exercises for cognitive training through a Pepper robot. We report on a user test with 24 older adults (65 +), which provided interesting and positive feedback regarding how the robot personalities have been exhibited and their impact on the experience of such users.

Exploring Robot Personality through Big Data Mining: A Century-Long Analysis from Google Books

Human–robot interactions (HRIs) are significantly influenced by the personality of robots. However, research on robot personality from the perspective of big data text mining remains scarce. To address this gap, our study delves into the portrayal of Big Five personality traits in robots across millions of Google Books, spanning from 1920 to 2019. In this study, we identify intriguing trends in how robot personalities have been described over the years. Notably, we observe that the trait of openness has consistently been the most frequently cited Big Five personality factor throughout the twentieth century. Following closely are conscientiousness, agreeableness, extraversion, and neuroticism. However, a noteworthy shift occurs in the late twentieth century, where extraversion garners increasing attention, ultimately becoming the most prominent Big Five personality factor after 2010. Furthermore, our analysis uncovers a fascinating positivity bias in the portrayal of robot personality. Robots are more commonly depicted as extroverted rather than introverted and open rather than reserved. These trends also correlate with the evolution of core personality words. For instance, the term intellectual robot gradually yields to intelligent robot over the course of the twentieth century. Additionally, in the twenty-first century, social robot emerges as the most prevailing topic. Understanding the interplay between human records and their perception of robot personalities provides valuable insights into both real descriptions and ideal expectations of robots. This research serves as a critical reference for further advancements in robot personality studies, shedding light on the dynamic nature of HRIs.

Classifying human emotions in HRI: applying global optimization model to EEG brain signals.

Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future.

The more extroverted the better? Unraveling the complex relationship between service robots' personality and the service robot experience

AbstractThis study combines the customer experience, service robot, and communication literatures to explore how a service robot's personality affects the overall service robot experience. It considers individual (here, the customers' extroverted vs. introverted personality) and situational (here, hedonic vs. utilitarian service settings) contextual factors, and three specific service robot experiences (here, warmth, competence, and discomfort). A pretest (n = 81) shows that a robot's personality (here, extroversion vs. introversion) can be shaped using verbal and nonverbal cues in the service robot design. Applying Complexity and Configuration Theories and using the novel fuzzy‐set qualitative comparative analysis and necessary condition analysis (NCA) techniques, the main study (n = 279) reveals the complex impact of the service robot personality on the overall service robot experience. Unlike conventional methods (e.g., SEM and regression) yielding averaged results, our findings show that various configurations with both introverted and extroverted robot personalities can equally lead to positive service robot experiences, based on the individual and situational context in which customers are embedded and the extent to which warmth, competence and discomfort are perceived. Hence, the more extroverted is not always better. Finally, the NCA findings reveal the minimum competence level that is required to obtain a certain level for the overall service robot experience. By understanding the intricate link between robot personality and its context, this research helps companies in strategically enhancing the service robot experience.

User Experience Design for Social Robots: A Case Study in Integrating Embodiment.

Social robotics is an emerging field with a high level of innovation. For many years, it was a concept framed in the literature and theoretical approaches. Scientific and technological advances have made it possible for robots to progressively make their way into different areas of our society, and now, they are ready to make the leap out of the industry and extend their presence into our daily lives. In this sense, user experience plays a fundamental role in achieving a smooth and natural interaction between robots and humans. This research focused on the user experience approach in terms of the embodiment of a robot, centring on its movements, gestures, and dialogues. The aim was to investigate how the interaction between robotic platforms and humans takes place and what differential aspects should be considered when designing the robot tasks. To achieve this objective, a qualitative and quantitative study was conducted based on a real interview between several human users and the robotic platform. The data were gathered by recording the session and having each user complete a form. The results showed that participants generally enjoyed interacting with the robot and found it engaging, which led to greater trust and satisfaction. However, delays and errors in the robot's responses caused frustration and disconnection. The study found that incorporating embodiment into the design of the robot improved the user experience, and the robot's personality and behaviour were significant factors. It was concluded that robotic platforms and their appearance, movements, and way of communicating have a decisive influence on the user's opinion and the way they interact.

Robot-Person Tracking in Uniform Appearance Scenarios: A New Dataset and Challenges

Person-tracking robots have many applications including security, surveillance, and autonomous driving. Despite the abundance of uniform appearance in many contexts and the challenges they exhibit, there is a lack of video datasets dedicated to benchmarking tracking algorithms in such contexts. In this article, we propose a new high-quality RGB-D benchmark called PTUA for robot–person tracking in uniform appearance scenarios. PTUA is recorded using an RGB-D sensor on top of a moving robot and consists of 45 sequences containing more than 85 K frames. Each frame is manually annotated with a bounding box and attributes, making PTUA the largest and the most challenging person tracking RGB-D dataset. To the best of our knowledge, such a densely annotated and properly synchronized RGB-D tracking benchmark does not exist in the literature. Each sequence comprises various challenges deriving from real-life scenarios where the target person appears highly similar to the background or distractors. By releasing PTUA, we expect to provide the community with a large-scale challenging RGB-D benchmark with high quality for the robust evaluation of trackers on uniform appearance scenarios for autonomous robots. We also present a rigorous experimental evaluation of the state-of-the-art trackers on the PTUA dataset with a comprehensive analysis. The findings evidence the challenges of person tracking in a uniform appearance scenario for both target tracking and robot–person tracking, and the need to bridge the performance gap. In addition, we propose a new RGB-D tracker that extracts features from RGB-D frames and it achieves the best performance on each challenging scenario of PTUA.

The effect of robot anthropomorphism on revisit intentions after service failure: a moderated serial mediation model

PurposeThe purpose of this study is to examine how robotic anthropomorphism and personalized design may affect consumers' reactions to brands after service failure.Design/methodology/approachThis study conducted two studies based on cognitive appraisal theory and artificial intelligence device acceptance theory. Study 1 explored the mechanisms by which the type of anthropomorphic design of the service robot (humanoid robot/nonhumanoid robot) influenced revisit intention after service failure through a one-factor between-subjects design based on a restaurant dining scenario. Study 2 was based on a hotel check-in scenario and explored the moderating effect of robot personalization design on the above mechanisms through a 2 (anthropomorphic design: humanoid robot/nonhumanoid robot) × 2 (personalized design: self-name/no name) between-subjects design.FindingsStudy 1 shows that consumers have higher performance expectations for nonhumanoid robots, leading to a higher tolerance for service failure, which in turn generates higher revisit intentions. Study 2 shows that consumers' performance expectations are significantly enhanced after custom naming of humanoid robots, so the serial mediation mechanism for the effect of robot anthropomorphic design on revisit intention does not hold.Originality/valueThis study extends the research of artificial intelligence device acceptance theory in the field of service failure and exploratively proposes an intervention mechanism for the negative effects of the anthropomorphic design of service robots.

Study of Telerobot Personalization for Children: Exploring Qualitative Coding of Artwork.

Social telepresence robots (i.e., telerobots) are used for social and learning experiences by children. However, most (if not all) commercially available telerobot bodies were designed for adults in corporate or healthcare settings. Due to an adult-focused market, telerobot design has typically not considered important factors such as age and physical aspect in the design of robot bodies. To better understand how peer interactants can facilitate the identities of remote children through personalization of robot bodies, we conducted an exploratory study to evaluate collaborative robot personalization. In this study, child participants (N=28) attended an interactive lesson on robots in our society. After the lesson, participants interacted with two telerobots for personalization activities and a robot fashion show. Finally, participants completed an artwork activity on robot design. Initial findings from this study will inform our continued work on telepresence robots for virtual inclusion and improved educational experiences of remote children and their peers.

An Emotional Model Based on Fuzzy Logic and Social Psychology for a Personal Assistant Robot

Personal assistants and social robotics have evolved significantly in recent years thanks to the development of artificial intelligence and affective computing. Today’s main challenge is achieving a more natural and human interaction with these systems. Integrating emotional models into social robotics is necessary to accomplish this goal. This paper presents an emotional model whose design has been supervised by psychologists, and its implementation on a social robot. Based on social psychology, this dimensional model has six dimensions with twelve emotions. Fuzzy logic has been selected for defining: (i) how the input stimuli affect the emotions and (ii) how the emotions affect the responses generated by the robot. The most significant contribution of this work is that the proposed methodology, which allows engineers to easily adapt the robot personality designed by a team of psychologists. It also allows expert psychologists to define the rules that relate the inputs and outputs to the emotions, even without technical knowledge. This methodology has been developed and validated on a personal assistant robot. It consists of three input stimuli, (i) the battery level, (ii) the brightness of the room, and (iii) the touch of caresses. In a simplified implementation of the general model, these inputs affect two emotions that generate an externalized emotional response through the robot’s heartbeat, facial expression, and tail movement. The three experiments performed verify the correct functioning of the emotional model developed, demonstrating that stimuli, independently or jointly, generate changes in emotions that, in turn, affect the robot’s responses.

To the question of civil legal personness of electronic persons

The article discusses approaches to determining the legal status, the possible legal capacity of electronic persons (robots) in civil law. Approaches on the possibility of applying the rules on animals or the legal status of a legal entity are analyzed. The author grounds that the material nature and the legal status of a computer program reflecting it are incompatible with the recognition of its autonomy of will and property independence. It is concluded that it is currently impossible to admit the legal personality of robots or electronic persons.

“I know how you feel”

Abstract In this article, we aim to evaluate the role of robots’ personality-driven behavioural patterns on users’ intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted with an empathic and self-comparative interaction style, and the other extroverted with a provocative and other-comparative interaction style. To evaluate the proposed technology acceptance model, we conducted an experiment (N = 209) at a public venue where users were requested to play a game with the support of the TIAGo robot. Our findings show that the robot personality affects the acceptance model and three relevant drivers: perceived enjoyment, perceived usefulness, and social influence. The extroverted robot was perceived as more useful than the introverted, and participants who interacted with it were faster at solving the game. On the other hand, the introverted robot was perceived as more enjoyable but less useful than the extroverted, and participants who interacted with it made fewer mistakes. Taken together, these findings support the importance of designing proper robot personalities in influencing users’ acceptance, featuring that a given style can elicit a different driver of acceptance.

The Influence of Personality Traits in Human‐Humanoid Robot Interaction

AbstractWith the advancement of science and technology, humanoid robots have been gradually adopted in human society. Given the human‐like appearance, the perceived personality of a humanoid robot can affect humans' general perceptions of the robotic agent, and even task performance. However, limited research pays attention to examining how a humanoid robot's non‐verbal cues characterize its personality traits. To solve this gap, this research aims to investigate how to use humanoid robots’ non‐verbal features (textual and gestural information) to develop different kinds of personality attributes, and how these personality characteristics affect human‐robot interaction. A total of 255 participants were recruited for this research, including pilot tests and two rounds of lab studies examining different levels of task complexity. The empirical results reveal the developed gestural cues allow a humanoid robot to distinguishably convey extrovert, ambivert, and introvert personality traits, where the perceived traits significantly affect user intentions to interact with the humanoid robot.