Walking in the uncanny valley: importance of the attractiveness on the acceptance of a robot as a working partner

Planning Socially Expressive Mobile Robot Trajectories

Many mobile robotics applications require robots to navigate around humans who may interpret the robot's motion in terms of social attitudes and intentions. It is essential to understand which aspects of the robot's motion are related to such perceptions so that we may design appropriate navigation algorithms. Current works in social navigation tend to strive towards a single ideal style of motion defined with respect to concepts such as comfort, naturalness, or legibility. These algorithms cannot be configured to alter trajectory features to control the social interpretations made by humans. In this work, we firstly present logistic regression models based on perception experiments linking human perceptions to a corpus of linear velocity profiles, establishing that various trajectory features impact human social perception of the robot. Secondly, we formulate a trajectory planning problem in the form of a constrained optimization, using novel constraints that can be selectively applied to shape the trajectory such that it generates the desired social perception. We demonstrate the ability of the proposed algorithm to accurately change each of the features of the generated trajectories based on the selected constraints, enabling subtle variations in the robot's motion to be consistently applied. By controlling the trajectories to induce different social perceptions, we provide a tool to better tailor the robot's actions to its role and deployment context to enhance acceptability.

Judging the emotional states of customer service staff in the workplace: A multimodal dataset analysis

Background

Emotions play a decisive and central role in the workplace, especially in the service-oriented enterprises. Due to the highly participatory and interactive nature of the service process, employees' emotions are usually highly volatile during the service delivery process, which can have a negative impact on business performance. Therefore, it is important to effectively judge the emotional states of customer service staff.

Methods

We collected data on real-life work situations of call center employees in a large company. Three consecutive studies were conducted: first, the emotional states of 29 customer service staff were videotaped by wide-angle cameras. In Study 1, we constructed scoring criteria and auxiliary tools of picture-type scales through a free association test. In Study 2, two groups of experts were invited to evaluate the emotional states of customer service staff. In Study 3, based on the results in Study 2 and a multimodal emotional recognition method, a multimodal dataset was constructed to explore how each modality conveys the emotions of customer service staff in workplace.

Results

Through the scoring by 2 groups of experts and 1 group of volunteers, we first developed a set of scoring criteria and picture-type scales with the combination of SAM scale for judging the emotional state of customer service staff. Then we constructed 99 (out of 297) sets of stable multimodal emotion datasets. Based on the comparison among the datasets, we found that voice conveys emotional valence in the workplace more significantly, and that facial expressions have more prominant connection with emotional arousal.

Conclusion

Theoretically, this study enriches the way in which emotion data is collected and can provide a basis for the subsequent development of multimodal emotional datasets. Practically, it can provide guidance for the effective judgment of employee emotions in the workplace.

The effect of service robot occupational gender stereotypes on customers' willingness to use them

Customers have obvious occupational gender stereotypes for service employees. In recent years, intelligent service robots have been widely used in the hospitality industry and have also been given gender characteristics to attract customers to use them. However, whether and when the usage of gendered service robots is effective remains to be explored. This research focuses on customers' occupational gender stereotypes and the gender of service robots, examining the influences of their consistency on customers' willingness to use service robots through three scenario studies. The findings suggest that: (1) The consistency between occupational gender stereotypes and service robot gender positively affects customers' willingness to use service robots. (2) Performance expectancy and trust are two psychological mechanisms underlying the above effect. (3) In the context of service failures, the consistency backfires and brings negative effects on willingness to use. This research extends the literature on customers' acceptance of anthropomorphized robots from the perspective of gender stereotypes and identifies the mechanisms behind the stereotype consistency effect. For practical implications, hotels should design and deploy gendered robots consistent with corresponding occupational gender stereotypes.

The deviation-from-familiarity effect: Expertise increases uncanniness of deviating exemplars

Humanlike entities deviating from the norm of human appearance are perceived as strange or uncanny. Explanations for the eeriness of deviating humanlike entities include ideas specific to human or animal stimuli like mate selection, avoidance of threat or disease, or dehumanization; however, deviation from highly familiar categories may provide a better explanation. Here it is tested whether experts and novices in a novel (greeble) category show different patterns of abnormality, attractiveness, and uncanniness responses to distorted and averaged greebles. Greeble-trained participants assessed the abnormality, attractiveness, uncanniness of normal, averaged, and distorted greebles and their responses were compared to participants who had not previously seen greebles. The data show that distorted greebles were more uncanny than normal greebles only in the training condition, and distorted greebles were more uncanny in the training compared to the control condition. In addition, averaged greebles were not more attractive than normal greebles regardless of condition. The results suggest uncanniness is elicited by deviations from stimulus categories of expertise rather than being a purely biological human- or animal-specific response.

Accepting Human-like Avatars in Social and Professional Roles

Humans report perceptions of unease or eeriness as humanoid/android robots and digital avatars approach human-like physical resemblance, a phenomenon alluded by the Uncanny Valley theory. This study extends the discussions on interactions and acceptance of digital avatars with findings from three experiments. In the first, perceptive evaluation of actors in clips from computer-generated animation and a live-action version of the same movie was examined. In the second experiment, we considered short clips with highly realistic digital avatars to measure recognition ability, the extent of eeriness, and specific physical features identified as unreal. The fixation area and pupil size variation recorded using an eye tracker were analyzed to infer attention to the body, face, and emotional response, respectively. Building on these findings, the third experiment looked at acceptance in roles requiring human skill, empathy, and cognitive ability. The results show that based on perceptions from physical attributes, the eeriness scores diverge from the uncanny valley theory as human-likeness increases. The realistic CGI and mocap technology could have helped cross the valley. Visual attention inferred from gaze behavior was similar for live-action and CGI. At the same time, we observe pupil size changes reflecting emotions like eeriness when the avatars either talked or smiled. Proficiency and acceptance scores were lower for roles requiring complex social cognition processes, such as friends and judicial decision-making. Interestingly, real-life stereotypes of gender roles were transferred to digital avatars too. The findings suggest an ambiguity in accepting human-like avatars in social and professional interactions, emphasizing the need for a multi-dimensional approach when applying the uncanny valley theory. A detailed and contextual examination is imperative as technological advancements have placed humans closer to co-existing with digital or physical android/humanoid robots.

A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity , and eeriness , and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance , and viewing duration . This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research.

Exploring the Perceptions of Cognitive and Affective Capabilities of Four, Real, Physical Robots with a Decreasing Degree of Morphological Human Likeness

This paper describes an investigation of student perceptions of the cognitive and affective capabilities of four robots that have a decreasing degree of morphological human likeness. We showed and illustrated the robots (i.e., InMoov, Padbot, Joy Robot and Turtlebot) to 62 students. After showing the students each of these robots, and explaining their main features and capabilities, we administered a fill-in questionnaire to the students. Our main hypothesis was that the perception of a robot’s cognitive and affective capabilities varied in correspondence with their appearance and in particular with their different degree of human likeness. The main results of this study indicate that the scores attributed to the cognitive and emotional capabilities of these robots are not modulated correspondingly to their different morphological similarity to humans. Furthermore, overall, the scores given to all of these robots regarding their ability to explicate mental functions are low, and even lower scores are given to their ability to feel emotions. There is a split between InMoov, the robot which has the highest degree of human likeness, and all of the others. Our results also indicate that: (1) morphological similarity of a robot to humans is not perceived automatically as such by observers, which is not considered a value in itself for the robot; and (2) even at lower levels of robot–human likeness, an uncanny valley effect arises but is quite mitigated by curiosity.

User Experience in Social Robots

Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and culture. However, introducing this technology for use in conventional environments is not trivial. For users to accept social robots, a positive user experience is vital, and it should be considered as a critical part of the robots' development process. This may potentially lead to excessive use of social robots and strengthen their diffusion in society. The goal of this study is to summarize the extant literature that is focused on user experience in social robots, and to identify the challenges and benefits of UX evaluation in social robots. To achieve this goal, the authors carried out a systematic literature review that relies on PRISMA guidelines. Our findings revealed that the most common methods to evaluate UX in social robots are questionnaires and interviews. UX evaluations were found out to be beneficial in providing early feedback and consequently in handling errors at an early stage. However, despite the importance of UX in social robots, robot developers often neglect to set UX goals due to lack of knowledge or lack of time. This study emphasizes the need for robot developers to acquire the required theoretical and practical knowledge on how to perform a successful UX evaluation.

Face the Uncanny: The Effects of Doppelganger Talking Head Avatars on Affect-Based Trust Toward Artificial Intelligence Technology are Mediated by Uncanny Valley Perceptions

This experiment (N = 228) examined how exposure to a talking head doppelganger created by an artificial intelligence (AI) program influenced affect-based trust toward AIs. Using a 3 (talking head featuring the participant's or a stranger's face, audio-only condition) by 2 (pro-AI pitch and anti-AI pitch playback) design, we uncovered that exposure to a talking head featuring the participant's face instead of a stranger's face increased uncanny valley perceptions. Furthermore, uncanny valley perceptions mediated the link between exposure to a talking head with the participant's face on affect-based trust. Overall, exposure to a doppelganger talking head, who delivered a persuasive pitch, triggered discomfort on the participant whose features were sourced to craft a synthetic talking head, which in turn decreased affect-based trust attributed to AIs. This phenomenon is rooted in basic psychological mechanisms that underpin the uncanny valley hypothesis. Future studies may test for these findings across different platforms and also provide evidence regarding user mental processing.

Social Robots on a Global Stage: Establishing a Role for Culture During Human–Robot Interaction

Robotic agents designed to assist people across a variety of social and service settings are becoming increasingly prevalent across the world. Here we synthesise two decades of empirical evidence from human–robot interaction (HRI) research to focus on cultural influences on expectations towards and responses to social robots, as well as the utility of robots displaying culturally specific social cues for improving human engagement. Findings suggest complex and intricate relationships between culture and human cognition in the context of HRI. The studies reviewed here transcend the often-studied and prototypical east–west dichotomy of cultures, and explore how people’s perceptions of robots are informed by their national culture as well as their experiences with robots. Many of the findings presented in this review raise intriguing questions concerning future directions for robotics designers and cultural psychologists, in terms of conceptualising and delivering culturally sensitive robots. We point out that such development is currently limited by heterogenous methods and low statistical power, which contribute to a concerning lack of generalisability. We also propose several avenues through which future work may begin to address these shortcomings. In sum, we highlight the critical role of culture in mediating efforts to develop robots aligned with human users’ cultural backgrounds, and argue for further research into the role of culturally-informed robotic development in facilitating human–robot interaction.

Designing Socially Assistive Robots for Alzheimer's Disease and Related Dementia Patients and Their Caregivers: Where We are and Where We are Headed

Over the past few years there has been a large rise in the field of robotics. Robots are being in used in many industries, but there has not been a large surge of robots in the medical field, especially the robots for healthcare use. However, as the aging population keeps growing, current medical staff and healthcare providers are increasingly burdened by caring for the ever-growing number of senior patients, especially those with cognitive impairment of Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) patients. As a result, we can expect to see a large increase in the field of medical robotics, especially in forms of socially assistive robots (SARs) for senior patients and healthcare providers. In fact, SARs can alleviate AD and ADRD patients and their caregivers' unmet medical needs. Herein, we propose a design outline for such a SAR, based on a review of the current literature. We believe the next generation of SARs will enhance health and well-being, reduce illness and disability, and improve quality of life for AD and ADRD patients and their caregivers.

Social Robotics and Engineering Students: Do They Match? Does Culture Matter?

This study investigates the level of familiarity and interest of students towards social robotics through a survey conducted with the Electrical Engineering and Computer Science Students at Heriot-Watt University in the UK and Electrical Engineering Students at Xidian University in China. The results indicate that whereas there is no significant difference in the level of familiarity within the three groups of students and no significant difference in the level of interest between the Electrical Engineering and Computer Science Students at Heriot Watt University, there is a statistically significant difference in the level of interest towards social robotics between the Heriot-Watt University and Xidian University Students. Xidian University Students demonstrate a higher level of interest towards social robotics. The qualitative analysis shows that many of the Xidian University Students are willing to perceive and have robots as companions whereas none of the Heriot-Watt University Students show such or similar tendency. This observation indicates that cultural background plays a significant role in interests and preferences of the students towards social robotics.

Upper- and lower-limb amputees show reduced levels of eeriness for images of prosthetic hands

The "uncanny phenomenon" describes the feeling of unease associated with seeing an image that is close to appearing human. Prosthetic hands in particular are well known to induce this effect. Little is known, however, about this phenomenon from the viewpoint of prosthesis users. We studied perceptions of eeriness and human-likeness for images of different types of mechanical, cosmetic, and anatomic hands in upper-limb prosthesis users (n=9), lower-limb prosthesis users (n=10), prosthetists (n=16), control participants with no prosthetic training (n=20), and control participants who were trained to use a myoelectric prosthetic hand simulator (n=23). Both the upper- and lower-limb prosthesis user groups showed a reduced uncanny phenomenon (i.e., significantly lower levels of eeriness) for cosmetic prosthetic hands compared to the other groups, with no concomitant reduction in how these stimuli were rated in terms of human-likeness. However, a similar effect was found neither for prosthetists with prolonged visual experience of prosthetic hands nor for the group with short-term training with the simulator. These findings in the prosthesis users therefore seem likely to be related to limb absence or prolonged experience with prostheses.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

Artificial intelligence in medicine

Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.