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The study presents an adaptive technique that enables a humanoid robot to select appropriate actions to maintain the engagement level of users while they play a serious game for cognitive training. The goal is to design and develop an adaptation strategy for changing the robot's behaviour based on Reinforcement Learning (RL) to encourage the user to remain engaged. Initially, we trained the algorithm in a simulated environment before moving on to a real user experiment. Thus, we first design, develop, and validate the RL strategy in a simulated environment. Subsequently, we integrate the trained policy into the robotic system, allowing it to select the best actions based on the detected user state during real user test. The RL algorithm was designed and implemented to determine an effective adaptation strategy for the robot's actions, encompassing verbal and non-verbal interactions. The proposed solution was first trained in a simulated environment and then tested with 28 users in a mixed-method design study. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractThe perceived similarity between human and robot personalities significantly impacts human–robot interaction (HRI), carrying ethical and psychological implications. This study examines the longitudinal evolution of personality similarity by analyzing Big Five personality trait descriptions in the Google Books corpus from 1960 to 2019. Our research revealed two primary findings: (a) Personality similarity demonstrated an overall increasing trend with a notable deceleration around 1980, potentially reflecting technological advancements and societal apprehensions; (b) A distinct hierarchy of trait similarities emerged. Openness exhibited the highest similarity to human personalities, followed by agreeableness, extraversion, neuroticism, and conscientiousness. This nuanced hierarchy suggests that human expectations of robotic personalities are not uniform across traits. By tracing historical perceptions of human-robot personality similarities, this study provides a comprehensive foundation for future HRI research and ethical considerations in robotics and artificial intelligence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractThis 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Social robots must master the nuances of human communication as a mean to convey an effective message and generate trust. It is well-known that non-verbal cues are very important in human interactions, and therefore a social robot should produce a body language coherent with its discourse. In this work, we report on a system that endows a humanoid robot with the ability to adapt its body language according to the sentiment of its speech. A combination of talking beat gestures with emotional cues such as eye lightings, body posture of voice intonation and volume permits a rich variety of behaviors. The developed approach is not purely reactive, and it easily allows to assign a kind of personality to the robot. We present several videos with the robot in two different scenarios, and showing discrete and histrionic personalities. [ABSTRACT FROM AUTHOR]

In the last years, museums around the world began using museum guide robots as an alternative to audio guides. Thus, it is important to identify how these new museum guides can optimally interact with visitors and whether they are more efficient that conventional audio guides. In this paper, we present the results from an experimental study which compared conventional audio guides with robots with different 'personality'. These results demonstrate that people remember significantly more information when they are guided by a cheerful robot than when their guide is a serious one or a conventional audio system. Importantly, we also introduce the idea of two collaborative tour guide robots. This idea was inspired by cognitive studies showing that people remember more when they receive information from two different human speakers. Interestingly, our participants also liked more our collaborative robots, than any version of a single robot and/or audio system. [ABSTRACT FROM AUTHOR]

Future service robots mass-produced for practical applications may benefit from having personalities. To engineer robot personalities in significant quantities for practical applications, we need first to identify the personality dimensions on which personality traits can be effectively optimised by minimising the distances between engineering targets and the corresponding robots under construction, since not all personality dimensions are applicable and equally prominent. Whether optimisation is possible on a personality dimension depends on how specific users consider the personalities of a type of robot, especially whether they can provide effective feedback to guide the optimisation of certain traits on a personality dimension. The dimensions may vary from user group to user group since not all people consider a type of trait to be relevant to a type of robot, which our results corroborate. Therefore, we had proposed a test procedure as an engineering tool to identify, with the help of a user group, personality dimensions for engineering robot personalities out of a type of robot knowing its typical usage. It applies to robots that can imitate human behaviour and small user groups with at least eight people. We confirmed its effectiveness in limited-scope tests.

Social robots must master the nuances of human communication as a mean to convey an effective message and generate trust. It is well-known that non-verbal cues are very important in human interactions, and therefore a social robot should produce a body language coherent with its discourse. In this work, we report on a system that endows a humanoid robot with the ability to adapt its body language according to the sentiment of its speech. A combination of talking beat gestures with emotional cues such as eye lightings, body posture of voice intonation and volume permits a rich variety of behaviors. The developed approach is not purely reactive, and it easily allows to assign a kind of personality to the robot. We present several videos with the robot in two different scenarios, and showing discrete and histrionic personalities.

Robot personalities are today painstakingly hand-crafted by content creators who specify motions, facial animations, and scripts in order to convey a unifying sense of character via interactions. While user preferences can be incorporated by modifying these behaviors to some degree, such as by inserting a user’s name or preference into a script, by and large new content must be continually created in order to maintain the robot’s ‘freshness.’ This content treadmill represents a long tail of work that diverts resources away from improving the underlying capabilities of the robot. Here we outline a novel method for defining robot personality that allows each robot to have and express a unique personality through varying behaviors that reduces the need for content creation. [ABSTRACT FROM AUTHOR]

With the development of social robots that are primarily designed for interacting with humans, particular facets of interaction need to be explored. One of them is the manifestation of robot personalities, which have the potential to raise acceptance and enhance user experience if done appropriate - or ruin both if done wrong. The present paper argues for the relevance of suitable robot personalities and discusses the factors that affect suitability, in particular interaction domain and personal preferences. An experiment (N = 30) with four different interaction scenarios (goal- and experience oriented) and three robot personalities (positive, neutral, negative) was performed to explore effects of personality and domain on personality suitability and acceptance. Results indicate that users can differentiate between different robot personalities and evaluate accordingly. In a goal-oriented stressful situation (train-ticket purchase under time pressure) the neutral personality was rated best. In experience-oriented scenarios, the positive robot personality was preferred. In the context of strictly performance oriented tasks, the effect of robot personality seems to be insignificant. Personal preferences for personalities seem to be influential, however, no clear pattern could be found. Lastly, directions for future research are depicted and implications for researchers and designers are discussed. [ABSTRACT FROM AUTHOR]

With the emerging application of social and psychological concepts to human-robot interaction, we investigated the effects of occupational roles (security vs. healthcare), gender (male vs. female), and personality (extrovert vs. introvert) on user acceptance of a social robot. In a laboratory experiment, a robot performed two different roles of a healthcare and security to address the potential usage of social robots at home. During the task, the robot manifested different genders and personalities via nonverbal cues. The results showed that participants (n = 164) preferred the robot with matching gender-occupational role and personality-occupational role stereotypes. This finding implies that the gender and personality of social robots do not monotonically influence user responses; instead, they interact with corresponding role stereotypes to affect user acceptance of social robots. In addition, personality-occupational role stereotypes showed a stronger effect on users' responses than gender-occupational role stereotypes. The overall results lay a foundation for designers to reduce the wide design spaces of social robots by grouping the various parameters under the big umbrella of social role stereotypes. [ABSTRACT FROM AUTHOR]

Abstract: The genetic robot has many configurable genes that contribute to defining the robot’s personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes MBTI-EAGRP. It is a fully autonomic gene-generative algorithm for a genetic robot’s personality in a mobile phone. After grasping the user preferences through MBTI assessment using the neural network algorithm, the evolutionary algorithm generates and evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features preferred by the user. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios and real MBTI measurements. [Copyright &y& Elsevier]

Abstract: This paper proposes a way to generate a robot genome that contributes to defining the personality of a software robot or an artificial life in a mobile phone. The personality should be both complex and feature-rich, but still plausible by human standards for an emotional life form. However, it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot''s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper proposes a neural network algorithm for a genetic robot''s personality (NNGRP) and an upgraded version of a previously introduced evolutionary algorithm for a genetic robot''s personality (EAGRP). The robot genomes for heterogeneous personalities are demonstrably generated via the NNGRP and the EAGRP and compared. The implementation is embedded into genetic robots in a mobile phone to verify the feasibility and effectiveness of each algorithm. [Copyright &y& Elsevier]

Abstract: This paper proposes a new concept of the genetic robot characterized by its own robot genome in which each chromosome consists of many genes that contribute to defining the robot’s personality. The large number of genes allows for a highly complex system, however it becomes increasingly difficult and time-consuming to ensure reliability, variability and consistency for the robot’s personality while manually initializing values for the individual genes. To overcome this difficulty, this paper also proposes an evolutionary algorithm for evolving a genetic robot’s personality (EAGRP) in a mobile phone. EAGRP evolves a gene pool that customizes the robot’s genome so that it closely matches a simplified set of personality features desired by the user. It acts on a two-dimensional individual, composed of artificial chromosomes, upon which a new masking method, the Eliza–Meme scheme, is used to derive a plausible individual given the restricted preference settings. This paper also proposes a crossover method that enables reproduction for the two-dimensional genome. Finally, an evaluation procedure for individuals is carried out in a virtual environment using tailored perception scenarios. [ABSTRACT FROM AUTHOR]

Future service robots mass-produced for practical applications may benefit from having personalities. To engineer robot personalities in significant quantities for practical applications, we need first to identify the personality dimensions on which personality traits can be effectively optimised by minimising the distances between engineering targets and the corresponding robots under construction, since not all personality dimensions are applicable and equally prominent. Whether optimisation is possible on a personality dimension depends on how specific users consider the personalities of a type of robot, especially whether they can provide effective feedback to guide the optimisation of certain traits on a personality dimension. The dimensions may vary from user group to user group since not all people consider a type of trait to be relevant to a type of robot, which our results corroborate. Therefore, we had proposed a test procedure as an engineering tool to identify, with the help of a user group, personality dimensions for engineering robot personalities out of a type of robot knowing its typical usage. It applies to robots that can imitate human behaviour and small user groups with at least eight people. We confirmed its effectiveness in limited-scope tests. [ABSTRACT FROM AUTHOR]

Empirical investigations on the uncanny valley have almost solely focused on the analysis of people's non-interactive perception of a robot at first sight. Recent studies suggest, however, that these uncanny first impressions may be significantly altered over an interaction. What is yet to discover is whether certain interaction patterns can lead to a faster decline in uncanny feelings. In this paper, we present a study in which participants with limited expertise in Computer Science played a collaborative geography game with a Furhat robot. During the game, Furhat displayed one of two personalities, which corresponded to two different interaction strategies. The robot was either optimistic and encouraging, or impatient and provocative. We performed the study in a science museum and recruited participants among the visitors. Our findings suggest that a robot that is rated high on agreeableness, emotional stability, and conscientiousness can indeed weaken uncanny feelings. This study has important implications for human-robot interaction design as it further highlights that a first impression, merely based on a robot's appearance, is not indicative of the affinity people might develop towards it throughout an interaction. We thus argue that future work should emphasize investigations on exact interaction patterns that can help to overcome uncanny feelings. • Crowd-authoring techniques enable to build coherent, personality-driven verbal and nonverbal behavior for a robot. • A robot's personality influences people's uncanny feelings towards it. • The longer people interact with a robot, the more the robot's behavior affects their perception. • Uncanny first impressions can decrease when interacting with a robot. [ABSTRACT FROM AUTHOR]