Android Robot Research Articles

Assessing the influence of an android robot's persuasive behaviors and context of violation on compliance

This study investigates how the perception of persuasive behaviors (polite, logical, displeased, angry) of an android robot is affected by situations regarding the context of violation (affecting oneself or others), and by subject traits, such as compliance awareness (CA) and agreeableness (AG). We conducted a video-based experiment based on a mixed-subjects design with 98 participants from the US and conducted a three-way mixed analysis of variance to investigate the impact of persuasive types and situation types (as within-subject factors), and the subject trait groups (CA or AG, as between-subject factors), on the subjective impressions of the persuasive behaviors by the android robot. Results showed that more negative behaviors (anger and displeasure) are appraised as being more appropriate and effective to persuade a violator in situations where the violation affects others, while no clear preference was found in a situation where the violation affects only oneself. Regarding the subject traits, participants with higher CA and lower AG would be willing to adhere to any persuasive behaviors, while their counterparts would dislike being persuaded through negative behaviors by the robot. These findings can be considered in future studies to develop cognitive models for generating situation-aware behaviors in social robots.

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.

Anthropomorphism-based artificial intelligence (AI) robots typology in hospitality and tourism

Purpose Anthropomorphism plays a crucial role in the deployment of human-like robots in hospitality and tourism. This study aims to propose an anthropomorphism-based typology of artificial intelligence (AI) robots, based on robot attributes, usage, function and application across different operational levels. Design/methodology/approach Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist, the research was conducted in two stages. A search strategy was implemented to explore anthropomorphism-based AI robots and to develop a robot typology. Findings This study provides a comprehensive typology of anthropomorphism-based AI robots used in tourism and hospitality and classifies them into four types, namely, chatbots, mechanoids, humanoids and android robots. Each type features distinct functions and applications. Practical implications The findings can assist companies in using anthropomorphic robots to improve service and strengthen competitiveness. This study offers valuable insights to managers for deploying AI robots across diverse service sectors. Originality/value This research provides a novel typology of hospitality and tourism AI robots and extends the understanding of anthropomorphism in human–robot interaction. This typology encompasses both virtual and physical robots, providing clarity on their attributes, usage, functions and applications across diverse areas of hospitality operations.

Impact of politeness and performance quality of android robots on future interaction decisions: a conversational design perspective.

Despite robots being applied in various situations of modern society, some people avoid them or do not feel comfortable interacting with them. Designs that allow robots to interact appropriately with people will make a positive impression on them resulting in a better evaluation of robots, which will solve this problem. To establish such a design, this study conducted two scenario-based experiments focusing on the politeness of the robot's conversation and behavior, and examined the impressions caused when the robot succeeds or slightly fails at a task. These two experiments revealed that regardless of whether the partner is a robot or a human, politeness not only affected the impression of interaction but also the expectations for better task results on the next occasion. Although the effect of politeness on preference toward robot agents was smaller than those toward human agents when agents failed a task, people were more likely to interact with polite robots and human agents again because they thought that they would not fail the next time. This study revealed that politeness motivates people to interact with robots repeatedly even if they make minor mistakes, suggesting that the politeness design is important for encouraging human-robot interaction.

Avoiding Existential Conflicts with Hyper Intelligent Computers

This Article presents my vision of the future where existential conflicts with intelligent computersand networks will be mitigated by the evolution of the human race from the intelligent people who invented the ‘dumb’ computers in the 1940s, to the smart people of 2010s who created ‘artificial intelligent’ computers (see reference [1]), and who through bioengineering built superhumans to face super-intelligent computers by 2040 [2], and to the builders of the future Hybrid humanoids (with organoid brains) who will evolve before 2100 to avoid conflicts with Hyper intelligent computers and of the Android robots (loaded with Avatars of humans) capable to deal with the omnipresent adversary distributed networks (Divergent networks).

Social perception of robots is shaped by beliefs about their minds

Roboticists often imbue robots with human-like physical features to increase the likelihood that they are afforded benefits known to be associated with anthropomorphism. Similarly, deepfakes often employ computer-generated human faces to attempt to create convincing simulacra of actual humans. In the present work, we investigate whether perceivers’ higher-order beliefs about faces (i.e., whether they represent actual people or android robots) modulate the extent to which perceivers deploy face-typical processing for social stimuli. Past work has shown that perceivers’ recognition performance is more impacted by the inversion of faces than objects, thus highlighting that faces are processed holistically (i.e., as Gestalt), whereas objects engage feature-based processing. Here, we use an inversion task to examine whether face-typical processing is attenuated when actual human faces are labeled as non-human (i.e., android robot). This allows us to employ a task shown to be differentially sensitive to social (i.e., faces) and non-social (i.e., objects) stimuli while also randomly assigning face stimuli to seem real or fake. The results show smaller inversion effects when face stimuli were believed to represent android robots compared to when they were believed to represent humans. This suggests that robots strongly resembling humans may still fail to be perceived as “social” due pre-existing beliefs about their mechanistic nature. Theoretical and practical implications of this research are discussed.

A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

ABSTRACT This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.

Self-administered questionnaires enhance emotion estimation of individuals with autism spectrum disorders in a robotic interview setting.

Robots offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Determining the optimal motion of robots when interacting with individuals with ASD is important for achieving more natural human-robot interactions and for exploiting the full potential of robotic interventions. Most prior studies have used supervised machine learning (ML) of user behavioral data to enable robot perception of affective states (i.e., arousal and valence) and engagement. It has previously been suggested that including personal demographic information in the identification of individuals with ASD is important for developing an automated system to perceive individual affective states and engagement. In this study, we hypothesized that assessing self-administered questionnaire data would contribute to the development of an automated estimation of the affective state and engagement when individuals with ASD are interviewed by an Android robot, which will be linked to implementing long-term interventions and maintaining the motivation of participants. Participants sat across a table from an android robot that played the role of the interviewer. Each participant underwent a mock job interview. Twenty-five participants with ASD (males 22, females 3, average chronological age = 22.8, average IQ = 94.04) completed the experiment. We collected multimodal data (i.e., audio, motion, gaze, and self-administered questionnaire data) to train a model to correctly classify the state of individuals with ASD when interviewed by an android robot. We demonstrated the technical feasibility of using ML to enable robot perception of affect and engagement of individuals with ASD based on multimodal data. For arousal and engagement, the area under the curve (AUC) values of the model estimates and expert coding were relatively high. Overall, the AUC values of arousal, valence, and engagement were improved by including self-administered questionnaire data in the classification. These findings support the hypothesis that assessing self-administered questionnaire data contributes to the development of an automated estimation of an individual's affective state and engagement. Given the efficacy of including self-administered questionnaire data, future studies should confirm the effectiveness of such long-term intervention with a robot to maintain participants' motivation based on the proposed method of emotion estimation.

Asymmetric communication: cognitive models of humans toward an android robot.

In the development of dialogue systems for android robots, the goal is to achieve human-like communication. However, subtle differences between android robots and humans are noticeable, leading even human-like android robots to be perceived differently. Understanding how humans accept android robots and optimizing their behavior is crucial. Generally, human customers have various expectations and anxieties when interacting with a robotic salesclerk instead of a human. Asymmetric communication arises when android robots treat customers like humans while customers treat robots as machines. Focusing on human-robot interaction in a tourist guide scenario, In this paper, we propose an asymmetric communication strategy that does not use estimation technology for preference information, but instead performs changing the agent's character in order to pretend to tailor to the customer. In line with this, we prepared an experimental method to evaluate asymmetric communication strategies, using video clips to simulate dialogues. Participants completed questionnaires without prior knowledge of whether the salesclerk was human-like or robotic. The method allowed us to assess how participants treated the salesclerk and the effectiveness of the asymmetric communication strategy. Additionally, during our demonstration in a dialogue robot competition, 29 visitors had a positive impression of the android robot's asymmetric communication strategy and reported a high level of satisfaction with the dialogue.

The effect of conversation on altruism: A comparative study with different media and generations.

Despite the overwhelming evidence of climate change and its effects on future generations, most individuals are still hesitant to make environmental changes that would especially benefit future generations. In this study, we investigate whether dialogue can influence people's altruistic behavior toward future generations of humans, and how it may be affected by participant age and the appearance of the conversation partner. We used a human, an android robot called Telenoid, and a speaker as representatives of future generations. Participants were split among an old age group and a young age group and were randomly assigned to converse with one of the aforementioned representatives. We asked the participants to play a round of the Dictator Game with the representative they were assigned, followed by an interactive conversation and another round of the Dictator Game in order to gauge their level of altruism. The results show that, on average, participants gave more money after having an interactive conversation, and that older adults tend to give more money than young adults. There were no significant differences between the three representatives. The results show that empathy might have been the most important factor in the increase in altruistic behavior for all participants.

Improving user's sense of participation in robot-driven dialogue

ABSTRACT In this study, we aimed to enhance the sense of dialogue participation (SDP), which reflects an individual's conscious engagement in dialogues, and investigated the multimodal dialogue elements that contribute to its improvement. To achieve this, we developed a dialogue system utilizing an android robot specifically for tour spot recommendations at a travel agency. The system was designed with various components aimed at enhancing SDP and was evaluated by 29 participants during the Dialogue Robot Competition 2022. After conceptually defining SDP, we employed the author's subjective evaluation to visualize the time-series changes in SDP at approximately 20-second intervals, using video recordings of the dialogues. Initially, we expected an overall decrease in SDP, as the robot had a speaking duration of around two minutes during the middle of the dialogue session when presenting the tour plan. However, the analysis of five participants' data revealed an increase in SDP during segments that involved ‘Dealing with content that increases the user's interest’ and ‘Multimodal response requests.’ Consequently, we can conclude that these elements have a discernible impact on enhancing SDP.

Human-like interactions prompt people to take a robot's perspective.

The increasing need for human-robot interaction requires not only robots to understand how humans think, but also humans to understand robots. Interestingly, little attention has been given to how humans interpret robots' behaviors. In this study, we adopted a social mental rotation task and investigated whether socially engaging behaviors could influence how people take a robot's perspectives. In a real lab, two android robots with neutral appearance sat opposite each other by a table with conflicting perspectives. Before the participant started the experiment, one of the robots behaved more interactively than the other by showing more socially engaging behaviors. Then the participant was required to identify rotated normal or mirrored digits presented inbetween the two robots. Results revealed a significant interactive effect between the digits type (normal; mirrored) and robot type (interactive; noninteractive). When digits were oriented to the interactive robot, we found a larger RT difference between normal and mirrored digits. In general, these findings suggested that robots' interactive behaviors could influence how people spontaneously consider the robot's perspective. Future studies may further consider how interactive behaviors can shape human-robot relationships and facilitate human-robot interaction.

Building a hospitable and reliable dialogue system for android robots: a scenario-based approach with large language models

Dialogue systems implemented with android robots are expected to provide not only advanced conversational ability but also reliability and hospitality due to their human-like appearance. In this study, we aim to develop a hospitable dialogue system by encouraging open-ended utterances and responding adaptively to give users the feeling of being heard. To achieve this, utilizing large language models (LLMs) is a promising option, but task-oriented dialogue systems implemented with only LLMs often generate irrelevant, inconsistent, or contradictory utterances. Therefore, we propose a scenario-based dialogue system that subdivides the task into smaller sub-tasks, such as summarization, information extraction, and response generation, and uses LLMs in a fine-grained manner to overcome such shortcomings of the LLM-based dialogue system. Our system was evaluated in the tourist-spot recommendation task of the Dialogue Robot Competition 2022 and achieved second place in the preliminary round and first place in the final round, outperforming other rule-based dialogue systems. However, we also identified several challenges when using LLMs for android dialogue systems, including response delays due to computational complexity, hallucinations, and coordination issues between generated utterances and robot control.

The XXI century: consciousness and the brain

The most remarkable result of human creativity in the XXI century is the development of creative android robots equipped with artificial intelligence. The rate of progress made in developing these robots is so fast that it will soon be impossible to distinguish robots from human beings in appearance features. Searching for the difference between humans and robots (tests by A. Turing, J. Searle, etc.) gives rise to a host of difficult questions concerning the common understanding of the definition of the term “consciousness”. However, the similarities between humans and robots can lead to a logical error, i.e., to substitution of actual reality for virtual reality.

Junior High Robotics Inspiration: Engaging Students with Exciting Interactive Socialization

This study elucidates the impact of a dynamic robot-focused socialization endeavor on the students of SMP Muhammadiyah 4 Porong. Targeting grades 7, 8, and 9, the initiative aimed to bridge the educational void resulting from the school's relocation. The presentation encompassed diverse national and international junior-level robot contests, along with rudimentary insights into robot components and their functionalities, aligning with the students' foundational aptitudes. Practical involvement, including hands-on interaction with an android smartphone-controlled sumo robot, generated enthusiasm reminiscent of play. A noteworthy upshot was the emergence of approximately 10 female students displaying a newfound interest in robotics. This venture not only augmented general knowledge but also spurred curiosity and engagement, emphasizing the potential for broader academic implications in the realm of junior high school education.
 Highlights:
 
 Interactive Exposure: The socialization event introduced students to national and international robot contests, fostering awareness and igniting interest in the field.
 Hands-on Experience: Through direct interaction with a smartphone-controlled sumo robot, students were engaged in a playful yet educational manner, encouraging active participation.
 Gender-Inclusive Impact: The initiative notably attracted around 10 female students, potentially contributing to gender diversity and inclusivity in the realm of robotics education.
 
 Keywords: Robotics, Junior High School, Socialization, Engagement, Sumo Robot

Social skills training using multiple humanoid robots for individuals with autism spectrum conditions.

Social skills training (SST) is used to help individuals with autism spectrum conditions (ASC) better understand the perspectives of others and social interactions, develop empathy skills, and learn how to engage with others socially. However, many individuals with ASC cannot easily sustain high motivation and concentration during such an intervention when it is administered by humans. We developed a social skills training program using multiple humanoid robots (STUH), including an android robot, that aimed to enable individuals with ASC to become familiar with the perspectives of others and improve their sociability and empathy skills. The objective of the present study was to investigate the effectiveness of STUH for these individuals. In STUH, we prepared 50 social exercises that consisted of conversations and behavioral interactions between an android robot and a simple humanoid robot. We prepared another humanoid robot that featured a cartoon-like and mechanical design, which played the role of host. In the first half-session of STUH, participants worked on the exercise from the perspective of an outsider. In the second half-session of STUH, they simulated experience by using robots as their avatars. The intervention associated with STUH was conducted for five days in total. We conducted an analysis of variance (ANOVA) featuring the intervention time point as the independent variable to examine changes in each score on the sociability index items. In total, 14 individuals with ASC participated in the study. The results of multiple comparison tests using the Bonferroni method indicated that all sociability index items improved between preintervention and follow-up. Our program enabled the participants to become familiar with the perspectives of others and improve their sociability. Given the promising results of this study, future studies featuring long-term follow-up should be conducted to draw definitive conclusions about the efficacy of our training system.

Adaptive learning in human–android interactions: an anthropological analysis of play and ritual

Using anthropological theory, this paper examines human–android interactions (HAI) as an emerging aspect of android science. These interactions are described in terms of adaptive learning (which is largely subconscious). This article is based on the observations reported and supplementary data from two studies that took place in Japan with a teleoperated android robot called Telenoid in the socialization of school children and older adults. We argue that interacting with androids brings about a special context, an interval, and a space/time for reflection and imagination that was not there before. During the interaction something happens. There is adaptive learning and as a result, both children and older adults accepted Telenoid, and the children and older adults accepted each other. Using frames of play and ritual, we make sense and ‘capture’ moments of adaptive learning, and the feedback that elicits a social response from all study participants that results in self-efficacy and socialization. While “ritual” refers to the application of what has been learned and “play” means that there are no obvious consequences of what has been learned. This analysis illuminates new understanding about the uncanny valley, cultural robotics and the therapeutic potential of HAI. This has implications for the acceptance of androids in ‘socialized roles’ and gives us insight into the subconscious adaptive learning processes that must take place within humans to accept androids into our society. This approach aims to provides a clearer conceptual basis and vocabulary for further research of android and humanoid development.

A Comprehensive Review: Interaction of Appearance and Behavior, Artificial Skin, and Humanoid Robot

The development of robotics is undeniable in recent years. Many developing contries face the growth of the elderly population, it is the premise and impetus for the development of research on humanoid robots to serve humans. Many studies on various aspects of robotics are carried out in different parts of the world. Many novel methods were introduced for the design of the robot’s external appearance, internal mechanisms, and gestures. Recent research on humanoid robots is designed to appear to be copies of the anthropometric indicators of real people, which may affect the security of other people’s identities. Besides, these robots cause a feeling of horror in the user if their appearance is in the position of the uncanny valley. Therefore, these designs need to be carefully considered before fabrication. Artificial skin is studied for various purposes such as ensuring collision safety for industrial users and helping robots to perceive basic tactile sensations. This review consists of the recent literature on the interaction of appearance and behavior of robot interaction, artificial skin, and especially humanoid robots, including appearance, such as android and Geminoid robots. This work can provide a reference for humanoid robot research, including uncanny valley hypotheses, artificial skin, and humanoid robots.

GEOECONOMICS AS A TOOL OF MODERN GEOSTRATEGY

The scholarly article presents different analytical approaches to modern understanding and manifestation forms of geoeconomics as a tool of geostrategy. It contains a comprehensive list of conceptual tools, empirical roots, and case studies on the political-economic essence of geoeconomics. The paper’s particular contribution is its direct connection with the practice and rooting of international political economy and poorly studied determination of geoeconomics with geostrategy in the evolving structure of the global world order and new vectors of geoeconomic dynamics of Ukraine. Based on the evolutionary method, the author has developed a matrix of approaches to understanding geoeconomics, demonstrated its progress, conducted a critical analysis of each of them, and put forward an original version and model of interpreting the relevant dynamic phenomenon following the strategic national interests of Ukraine. The myth of static assumptions about the existence of geoeconomics as a self-sufficient system that should have replaced geopolitics was debunked. Using the case study method, the key factors facilitating the geoeconomic expansion of China, the USA, and Russia are highlighted. Emphasis is placed on the role of other states in the formation of geoeconomics, and the issue of strategic autonomy is elucidated. The author justifies the genuine interest in geoeconomics as a component of geostrategy, demonstrates its determination, and specifies the main modern tools of geoeconomics. The impact of the military discourse on geoeconomics is proven, and the vectors of geoeconomic de-sovereignization of some countries in the context of economic globalization, geosecurity expansion, and the growing role of services in the GDP structure. The importance of studying the role of artificial intelligence, android robots and humanoids in GDP formation was separately emphasized; options of their influence on the world’s geoeconomics and the development of humanity as a whole amidst neo-economics are modeled.

Assessing the effect of dialogue on altruism toward future generations: A preliminary study

IntroductionDespite the abundance of evidence on climate change and its consequences on future generations, people, in general, are still reluctant to change their actions and behaviors toward the environment that would particularly benefit posterity. In this study, we took a preliminary step in a new research direction to explore humans' altruistic behavior toward future generations of people and whether it can be affected by dialogue.MethodsWe used an android robot called Telenoid as a representative of future generations by explaining that the robot is controlled by an Artificial Intelligence (AI) living in a simulation of our world in the future. To measure people's altruistic behavior toward it, we asked the participants to play a round of the Dictator Game with the Telenoid before having an interactive conversation with the Telenoid and then playing another round.ResultsOn average, participants gave more money to the Telenoid in the second round (after having an interactive conversation). The average amount of money increased from 20% in the first to about 30% in the second round.DiscussionThe results indicate that the conversation with the robot might have been responsible for the change in altruistic behavior toward the Telenoid. Contrary to our expectations, the personality of the participants did not appear to have an influence on their change of behavior, but other factors might have contributed. We finally discuss the influence of other possible factors such as empathy and the appearance of the robot. However, the preliminary nature of this study should deter us from making any definitive conclusions, but the results are promising for establishing the ground for future experiments.