Human-like Appearance Research Articles

Ethics of robotized public services: The role of robot design and its actions

Public administrations invest heavily in the development of ‘smart’ public services, including autonomous public service robots. Since public service robots are designed to operate unsupervised, robots must interact in an ethically acceptable way with citizens. Robots are often designed to provide a comfortable interaction with citizens, which can be achieved by making the robot's appearance and actions more human-like. This raises the question whether a human-like design affects the ethicalness evaluation of a robot's actions. In a laboratory experiment with eye-tracking (n1 = 156) and a representative, online vignette experiment (n2 = 1339), we find that a more human-like robot design draws more visual attention than a robot with a less human-like design. However, the robot's appearance does not affect the ethicalness evaluation of the robot's behavior. In contrast, our results show that it is not the more human-like appearance that influences evaluations of ethicalness, but a robot's ethical actions influence the extent to which it is perceived as human. We frame our findings in the scientific and practitioner debates on ethical rule-setting for (public) service robots.

Attitudes toward service robots: analyses of explicit and implicit attitudes based on anthropomorphism and construal level theory

PurposeBuilding on both the uncanny valley and construal level theories, the analyses detailed in this paper aims to address customers’ explicit and implicit attitudes toward various service robots, categorized by the degree of their human-like appearance, namely, mechanoids (low human-likeness), humanoids (medium human-likeness) and realistic robots (high human-likeness).Design/methodology/approachThe analyses reflect a mixed-method approach, across three studies. A qualitative study uses focus groups to identify consensual attitudes. An experiment measures self-reported, explicit attitudes toward the three categories of robots. Another experiment explores customers’ implicit attitudes (unconscious and unintentional) toward robots, using three implicit association tests.FindingsCustomers express both positive and negative attitudes toward service robots. The realistic robots lead to both explicit and implicit negative attitudes, suggesting that customers tend to reject these robots in frontline service settings. Robots with lower human-likeness levels generate relatively more positive attitudes and are accepted to nearly the same extent as human employees in hospitality and tourism contexts.Practical implicationsBecause customers reject, both consciously and unconsciously, very human-like robots in service encounters, managers should leverage this key finding, along with the more detailed results, to inform their strategic introduction of robots into hospitality frontline service settings.Originality/valueThe combined qualitative and quantitative studies specify and clarify customers’ implicit and explicit attitudes toward robots with different levels of human-likeness, in the real-world setting of hospitality and tourism services. Such insights can inform continued research into the effects of these service innovations.

Impact of anthropomorphic features of artificially intelligent service robots on consumer acceptance: moderating role of sense of humor

PurposeThis study aims to investigate the impact of both physical and personality-related anthropomorphic features of an artificial intelligence service robot on the cognitive and affective appraisals and acceptance of consumers during service delivery.Design/methodology/approachThe proposed hypotheses that investigate the effects of service robots’ physical appearance on the emphasis consumers place on each evaluation criteria they use in determining their willingness to accept the use of service robots in service delivery and the moderating role of sense of humor are tested by conducting two studies using scenario-based experiments.FindingsThe results show that humanlike appearance leads to higher performance expectancy, mascot-like appearance generates higher positive emotions and machine-like appearance results in higher effort expectancy. The effects of humanlike and mascot-like appearances on consumer acceptance are moderated by the sense of humor of service robots. However, the sense of humor effect is attenuated with a machine-like appearance owing to the lack of anthropomorphism.Practical implicationsThis study provides crucial insights for hospitality managers who plan to use service robots in service delivery. The findings highlight the key roles of appearance type and sense of humor of service robots in influencing the appraisals and acceptance of consumers regarding the use of service robots in service delivery.Originality/valueThis study focuses on comparing the effects of traditional and mascot-like appearances of service robots on consumer appraisals and identifies sense of humor as a cute anthropomorphized personality trait of service robots.

Pre-service teachers’ concerns about social robots in the classroom: A model for development

Social robots are being tested in the educational arena with current thinking in two main directions. One is arguing for the benefits of robots in affective and efficient instruction and is more teachercentered. Within the second, more student-centered oriented, proponents of human uniqueness are raising long-term concerns. Teacher-centeredness and student-centeredness form pedagogical beliefs underpinning teachers’ attitudes guiding technology integration. Limited research has explored teachers’ underlying beliefs and attitudes to social robots, with some presenting mixed feelings identifying some concerns with some identifying more positive attitudes. Preservice education is critical in forming beliefs, and this paper presents a qualitative study of Slovene preservice pre-primary school and primary classroom pre-service teachers’ attitudes and underlying beliefs. Students were asked to reflect on their perception of social robotic educational technology in which they would highlight at their own discretion the positive, neutral and negative aspects. Students’ reflections predominantly expressed concerns. The research model was designed in part, drawing from participants reflections and on related studies. Previous studies indicated the concerns teachers hold about robotic technology, but lacked a more holistic model. We built a threefold model distinguishing instructional, social-emotional, and legal concerns. Our findings differ from related studies because they identified participants’ negative attitudes and a clear rejection of robot technology with a human-like appearance and social skills in the classroom. Previous studentcentered studies reported on single groups of concerns within specific contexts without developing a holistic view relating diverse concerns in one picture. Related teacher-centered studies were arguing for refinements anticipating robot’s social intelligence affordance in the classroom. The participants in our study are not rejecting social robots as such, but in their view, the robot is not granted the status of a social entity capable of engaging in student-centered teaching and taking care of child wellbeing and development. The findings of our study call for action and informed robot development, taking into consideration teachers as co-designers.

I Am Looking for Your Mind: Pupil Dilation Predicts Individual Differences in Sensitivity to Hints of Human-Likeness in Robot Behavior.

The presence of artificial agents in our everyday lives is continuously increasing. Hence, the question of how human social cognition mechanisms are activated in interactions with artificial agents, such as humanoid robots, is frequently being asked. One interesting question is whether humans perceive humanoid robots as mere artifacts (interpreting their behavior with reference to their function, thereby adopting the design stance) or as intentional agents (interpreting their behavior with reference to mental states, thereby adopting the intentional stance). Due to their humanlike appearance, humanoid robots might be capable of evoking the intentional stance. On the other hand, the knowledge that humanoid robots are only artifacts should call for adopting the design stance. Thus, observing a humanoid robot might evoke a cognitive conflict between the natural tendency of adopting the intentional stance and the knowledge about the actual nature of robots, which should elicit the design stance. In the present study, we investigated the cognitive conflict hypothesis by measuring participants’ pupil dilation during the completion of the InStance Test. Prior to each pupillary recording, participants were instructed to observe the humanoid robot iCub behaving in two different ways (either machine-like or humanlike behavior). Results showed that pupil dilation and response time patterns were predictive of individual biases in the adoption of the intentional or design stance in the IST. These results may suggest individual differences in mental effort and cognitive flexibility in reading and interpreting the behavior of an artificial agent.

Social Correlates of and Reasons for Primate Meat Consumption in Central Amazonia

Traditionally, humans have consumed nonhuman primates in many places, including throughout the Amazon region. However, primate consumption rates are changing with rising urbanization and market access. We characterize primate consumption in central Amazonia using 192 qualitative interviews with inhabitants in three rural villages and in the city of Tefé. We used a generalized linear model to investigate how individual consumer characteristics, such as age and gender, and livelihoods affected primate consumption. We also used principal coordinate analysis (PCoA), and word clouds and network text analyses, to describe reasons people gave for eating or avoiding primates. Our results show that men were more likely to say that they eat primates than women, and that the probability that a person said that they eat primates correlated positively with the percentage of their life lived in rural areas. People gave sentiment and ethical reasons not to eat primates. Custom influenced whether people said they eat primates both positively and negatively, while taste positively influenced whether people said they eat primates. A preference for other wild meats in rural areas, and for domestic meats in cities negatively influenced whether people said they eat primates. People also cited the perceptions that primates have a human-like appearance and that primate meat is unhealthy as reasons not to eat primates. People in urban areas also cited conservation attitudes as reasons for not eating primates. Our findings provide an understanding of factors influencing primate consumption in our study area and will be useful for designing tailored conservation initiatives by reducing hunting pressure on primates in rural settings and increasing the effectiveness of outreach campaigns in urban centers.

Research and Implementation of a Humanoid Robot Control Method Based on Action Mapping

In order to reduce programming difficulty and let humanoid robot act more humanlike, we designed a robotics control method for humanoid robots. Firstly, based on human body movements collected by Kinect camera, we calculated 3D data of joint point with pinhole model, and established space model of robot and skeleton model of human. Then, we transformed 3D coordinates of human joints into joint values by space vector method, mapping to the joints of the robot and guides the robot to move. Finally, with semi-autonomous control experiment, it shows that humanoid robot action mapping strategy is feasible and effective. Compared with the existing similar research, this paper implements the action guidance control of humanoid robot, enhances the intelligent degree of robot and expands the application scene and scope of humanoid robot. Depending on the human-like appearance, humanoid robot with action mapping could provide a much more intelligent interaction method.

Friends are not to be eaten: Children are reluctant to eat cookies that share physical and psychological human features due to their desire to relate to the food

Children's food marketers very often use anthropomorphism in advertising as if it is an effective technique aimed at increasing food consumption. However, the evidence supporting this effect is mixed. In this research, we propose that while a food product's humanlike appearance (physical anthropomorphism) may increase consumption, attribution of mental states and emotions (psychological anthropomorphism) to a physically anthropomorphized food product discourages consumption because it facilitates the formation of a social-like relationship with the food. In two studies, we tested this prediction on samples of children. Preschoolers (N = 91 and N = 97) were randomly assigned to one of three conditions in which they were presented with a cookie that was both physically and psychologically anthropomorphized, physically anthropomorphized only, or not anthropomorphized. In two studies, we observed whether children left alone with the cookie would consume it and how quickly it would happen. Additionally, in Study 2, we asked children about their desire to relate to the cookie. The results confirmed that physical anthropomorphism accompanied by psychological anthropomorphism considerably reduced consumption of the cookies compared to both mere physical anthropomorphism and non-anthropomorphism. Moreover, the reduced appetite for cookies that were physically and psychologically anthropomorphized was mediated by the desire to relate to them. We discuss the implications of these results.

A set of distinct facial traits learned by machines is not predictive of appearance bias in the wild

Research in social psychology has shown that people’s biased, subjective judgments about another’s personality based solely on their appearance are not predictive of their actual personality traits. But researchers and companies often utilize computer vision models to predict similarly subjective personality attributes such as “employability”. We seek to determine whether state-of-the-art, black box face processing technology can learn human-like appearance biases. With features extracted with FaceNet, a widely used face recognition framework, we train a transfer learning model on human subjects’ first impressions of personality traits in other faces as measured by social psychologists. We find that features extracted with FaceNet can be used to predict human appearance bias scores for deliberately manipulated faces but not for randomly generated faces scored by humans. Additionally, in contrast to work with human biases in social psychology, the model does not find a significant signal correlating politicians’ vote shares with perceived competence bias. With Local Interpretable Model-Agnostic Explanations (LIME), we provide several explanations for this discrepancy. Our results suggest that some signals of appearance bias documented in social psychology are not embedded by the machine learning techniques we investigate. We shed light on the ways in which appearance bias could be embedded in face processing technology and cast further doubt on the practice of predicting subjective traits based on appearances.

Empirical evaluation and pathway modeling of visual attention to virtual humans in an appearance fidelity continuum

In this contribution we studied how different rendering styles of a virtual human impacted users’ visual attention in an interactive medical training simulator. In a mixed design experiment, 78 participants interacted with a virtual human representing a sample from the non-photorealistic (NPR) to the photorealistic (PR) rendering continuity. We presented five rendering style samples scenarios, namely All Pencil Shaded (APS), Pencil Shaded (PS), All Cartoon Shaded (ACT), Cartoon Shaded (CT), and Human-Like (HL), and compared how visual attention differed between groups of users. For this study, we employed an eye tracking system for collecting and analyzing users’ gaze during interaction with the virtual human in a failure to rescue medical training simulation. Results shows that users spent more total time in the APS and ACT conditions but users visually attended more to virtual humans in the PS, CT and HL appearance conditions.

I, Robot: How Human Appearance and Mind Attribution Relate to the Perceived Danger of Robots

Social robots become increasingly human-like in appearance and behaviour. However, a large body of research shows that these robots tend to elicit negative feelings of eeriness, danger, and threat. In the present study, we explored whether and how human-like appearance and mind-attribution contribute to these negative feelings and clarified possible underlying mechanisms. Participants were presented with pictures of mechanical, humanoid, and android robots, and physical anthropomorphism (Studies 1–3), attribution of mind perception of agency and experience (Studies 2 and 3), threat to human–machine distinctiveness, and damage to humans and their identity were assessed for all three robot types. Replicating earlier research, human–machine distinctiveness mediated the influence of anthropomorphic appearance on the perceived damage for humans and their identity, and this mediation was due to anthropomorphic appearance of the robot. Perceived agency and experience did not show similar mediating effects on human–machine distinctiveness, but a positive relation with perceived damage for humans and their identity. Possible explanations are discussed.

Matter over mind? How the acceptance of digital entities depends on their appearance, mental prowess, and the interaction between both

Abstract Digital technologies are advancing rapidly, growing to be more human-like and intelligent by the day. However, research shows that a machine's resemblance to humans can reach a critical level, which makes it seem uncanny to observers. While scholars have discussed this effect in terms of both human-like appearances and mental abilities, a potential interaction between the two aspects has hardly been addressed in literature. We designed a two-factorial experiment to overcome the identified research gap, introducing participants to digital agents with varying embodiment (text interface/human rendering) and mental capacity (simple algorithms/complex artificial intelligence). Our results show that the interaction of both factors indeed affects participants’ experience in a crucial way: Whereas an agent based on simple algorithms only evokes discomfort when embedded in a human-like body, the artificial intelligence is always perceived as eerie, regardless of its embodiment. Yet, additional findings raise doubts on the unidimensionality of participants’ affective response.

Effects of Robot’s Awareness and its Subtle Reactions Toward People’s Perceived Feelings in Touch Interaction

This study addresses the effects of a robot’s awareness and its subtle reactions toward the perceived feelings of people who touch a robot. When another unexpectedly touches us, we subtly and involuntarily react. Because such reactions are involuntary, it is impossible to eliminate them for humans. However, intentionally using them for robots might positively affect their perceived feelings, in particular, when a robot has a human-like appearance that evokes human-like reactions. We investigate the relationship between subtle reactions and the awareness of the existence of a human, i.e., whether a robot’s awareness and its subtle reactions influence people’s impressions of the robot when they touch it. Our experimental results with 20 participants and an android with a female-like appearance showed significant effects between awareness and subtle reactions. When the robot did not show awareness, its subtle reaction increased the perceived human-likeness. Moreover, when the robot did not show subtle reactions, showing awareness beforehand increased the perceived human-likeness.

Multi-Modal Interaction Through Anthropomorphically Designed Communication Medium to Enhance the Self-Disclosures of Personal Information

Although current communication media facilitate interactions among individuals, researchers have warned that human relationships built through these media tend to lack the level of intimacy acquired through face-to-face communications. In this study, we investigate how the long-term use of humanlike communication media affects the development of intimate relationships between human users. We examine changes in the relationship between individuals while they converse with each other through humanlike communication media or mobile phones for approximately a month. The intimacy of their relationship was evaluated using the amount of self-disclosed personal information. The result shows that a significantly greater amount of self-disclosure is made through a communication medium with humanlike appearance and soft material compared with the use of a typical mobile phone. The amount of self-disclosure showed cyclic variation in the experiment through humanlike communication media. Furthermore, we discuss a possible underlying mechanism of this effect from the misattribution of a feeling caused by intimate distance with the medium to a conversation partner.

Planning Grasping Motions for Humanoid Robots

This paper addresses the problem of obtaining the required motions for a humanoid robot to perform grasp actions trying to mimic the coordinated hand–arm movements humans do. The first step is the data acquisition and analysis, which consists in capturing human movements while grasping several everyday objects (covering four possible grasp types), mapping them to the robot and computing the hand motion synergies for the pre-grasp and grasp phases (per grasp type). Then, the grasp and motion synthesis step is done, which consists in generating potential grasps for a given object using the four family types, and planning the motions using a bi-directional multi-goal sampling-based planner, which efficiently guides the motion planning following the synergies in a reduced search space, resulting in paths with human-like appearance. The approach has been tested in simulation, thoroughly compared with other state-of-the-art planning algorithms obtaining better results, and also implemented in a real robot.

Effect of Anthropomorphic Design on the Effectiveness of Motivational Messages

The potential for social robots to provide assistance to those in need continues to increase though optimal design for user acceptance and engagement continues to be elusive. The design of social robots for user motivation specifically has not been well researched yet evidence does indicate that virtual agents can be motivational. Theory of mind and social facilitation research points to the motivational potential of social robots. This study examines the effect of human-like appearance (i.e. anthropomorphism) on the effectiveness of motivational messages. Results indicate that images of humans paired with motivational messages were perceived as significantly more motivating than anthropomorphic images and zoomorphic images paired with motivational messages. There were no differences in motivation between anthropomorphic and zoomorphic images. Data indicates that participants tended to prefer human images to anthropomorphic and zoomorphic images for motivational purposes.

When Humanizing Customer Service Chatbots Might Backfire

Abstract More and more companies are using chatbots in customer service. Instead of with a human employee, customers interact with a machine. Many companies give these chatbots human traits through names, human-like appearances, a human voice or even character descriptions. Intuitively such a humanization strategy seems to be a good idea. Studies show, however, that the humanization of chatbots is perceived in a nuanced way and can also backfire. Especially in the context of customer complaints, human-like chatbots can intensify negative reactions of angry customers, because their performance is judged more critically compared to non-humanized chatbot variants. Service managers should therefore consider very carefully whether and in which situations they should use humanized service chatbots.

What Kinds of Robot's Touch Will Match Expressed Emotions?

This study investigated the effects of touch characteristics that change the strength and the naturalness of the emotions perceived by people in human-robot touch interaction with an android robot that has a feminine, human-like appearance. Past studies on human-robot touch interaction focused on understanding what kinds of human touches conveyed emotion to robots, i.e., the robot's touch characteristics that can affect people's perceived emotions received less focus. In this study, we concentrated on three touch characteristics (length, type, and part) based on arousal/valence perspectives, and their effects on the perceived strength/naturalness of a commonly used emotion in human-robot interaction, i.e., happiness, and its counterpart emotion, (i.e., sadness), borrowing Ekman's definitions. Our results showed that the touch length and its type are useful to change the perceived strengths and the naturalness of the expressed emotions based on the arousal/valence perspective, although the touch part did not fit such perspective assumptions. Finally, our results suggest that a brief pat and a longer contact by the fingers are better combinations to express happy and sad emotions with our robot. Since we only used a female android, we discussed future works with a male humanoid robot and/or a robot whose appearance is less humanoid.

Development of an Effective Information Media Using Two Android Robots

Conversational robots have been used to convey information to people in the real world. Android robots, which have a human-like appearance, are expected to be able to convey not only objective information but also subjective information, such as a robot’s feelings. Meanwhile, as an approach to realize attractive conversation, multi-party conversation by multiple robots was the focus of this study. By collaborating among several robots, the robots provide information while maintaining the naturalness of conversation. However, the effectiveness of interaction with people has not been surveyed using this method. In this paper, to develop more efficient media to convey information, we propose a scenario-based, semi-passive conversation system using two androids. To verify its effectiveness, we conducted a subjective experiment comparing it to a system that does not include any interaction with people, and we investigated how much information the proposed system successfully conveys by using a recall test and a questionnaire about the conversation and androids. The experimental results showed that participants who engaged with the proposed system recalled more content from the conversation and felt more empathic concern for androids.

The Dilemma of Openness in Social Robots

This paper conducts a philosophical inquiry into past empirical research that reveals emotional coupling and category confusion between the human and the social robot. It examines whether emotional coupling and category confusion would increase or diminish the reification of human emotion and the human milieu by examining whether they fulfill the ideal of openness in technology. The important theories of openness, from the respective proposals of open industrial machines by Gérard-Joseph Christian and Karl Marx, to Umberto Eco’s critique of open art and Gilbert Simondon’s philosophy of open technology, are in agreement that (i) openness is the condition for realizing the potentiality for transcending the existing aesthetic, technical, or social structure, and (ii) that the realization of potentiality would diminish the reification of the human milieu. The therapeutic effect of emotional coupling with social robots seems to fulfill this ideal of open technology, whereas category confusion seems to increase rather than diminish reification. If people confuse the robot with the human, they risk losing sight of the unpredictability of other human beings that is essential to human development. This paper concludes that it is possible to avoid category confusion by building social robots without giving them a human-like appearance.