Non-humanoid Robots Research Articles

Effects of robots’ appearance on guest service experiences

ABSTRACT Robots are starting to replace human employees in the accommodation industry. It remains unclear how guests perceive robots. The study aims to evaluate the effectiveness of the humanoid and non-humanoid robots and to compare the guests’ perceptions. The data was collected by web digging and analyzed using text mining and machine learning. The results show that guests experience more positive emotions in non-humanoid robotic experiences. They also stated that they found both types of robot fun and interesting. However, guests have negative perceptions about the perceived usefulness and appearance of humanoid robots. According to the findings of the machine learning analysis, the neural network and logistic regression models show the best statistical performance in both datasets compared to other machine learning models. The study contributes to the literature through an investigation of guests’ opinions regarding robot types with the use of machine learning and text mining methods.

Role-taking and robotic form: an exploratory study of social connection in human-robot interaction

Human-robot interaction (HRI) spans many and diverse contexts, each varying in the preferred degree of social connection fostered therein. The design of robotic systems and selection of which robotic forms to use will thus benefit from an understanding of the factors by which social connection is enhanced or diminished. We address this fundamental problem through an exploratory study of role-taking, a core construct from structural social psychology. Through a laboratory experiment (N = 86) in which participants interact with either human, humanoid robot, or non-humanoid robot partners, we examine variation in perceived role-taking accuracy, affection for the partner, and desire for continued interaction. Findings show that participants evaluate human interaction partners as more accurate role-takers than robots, but with variation between robotic types (humanoid vs non-humanoid) in relation to personality versus emotion. Sense of affection and desire for continued interaction are predicted by interpersonal factors rather than partner type or robotic form. We discuss possible reasons behind these preliminary findings and draw on them to formulate subsequent research questions. More broadly, we call for continued study at the intersection of HRI and structural social psychology, advancing both fields through the pairing.

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

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

People Do Not Always Know Best: Preschoolers’ Trust in Social Robots

ABSTRACT In this paper, we investigated whether Canadian preschoolers prefer to learn from a competent robot over an incompetent human using the classic trust paradigm. An adapted Naive Biology task was also administered to assess children’s perception of robots. In Study 1, 3-year-olds and 5-year-olds were presented with two informants; A social, humanoid robot (Nao) who labeled familiar objects correctly, while a human informant labeled them incorrectly. Both informants then labeled unfamiliar objects with novel labels. It was found that 3-year-old children equally endorsed the labels provided by the robot and the human, but 5-year-old children learned significantly more from the competent robot. Interestingly, 5-year-olds endorsed Nao’s labels even though they accurately categorized the robot as having mechanical insides. In contrast, 3-year-old children associated Nao with biological or mechanical insides equally. In Study 2, new samples of 3-year-olds and 5-year-olds were tested to determine whether the human-like appearance of the robot informant impacted children’s trust judgments. The procedure was identical to that of Study 1, except that a non-humanoid robot, Cozmo, replaced Nao. It was found that 3-year-old children still trusted the robot and the human equally and that 5-year-olds preferred to learn new labels from the robot, suggesting that the robot’s morphology does not play a key role in their selective trust strategies. It is concluded that by 5 years of age, preschoolers show a robust sensitivity to epistemic characteristics (e.g., competency), but that younger children’s decisions are equally driven by the animacy of the informant.

How consumers react differently toward humanoid vs. nonhumanoid robots after service failures: a moderated chain mediation model

PurposeWith the increasing use of robots in service scenarios in hospitality industries, service failure frequently occurs during the service process, and consumers may react differently toward humanoid vs. nonhumanoid robots due to different performance expectancies. This study focuses on consumers' reactions to service failures by humanoid vs. nonhumanoid robots and the different impacts on brand forgiveness and revisit intentions through performance expectancy for different genders.Design/methodology/approachThe study used a sample of 280 participants to test the moderated chain mediation model. The participants were instructed to report their performance expectancies for humanoid/nonhumanoid robots and imagine a hotel check-in scenario in which a service failure occurs. Brand forgiveness, brand revisit intention and other demographic information were assessed.FindingsThe results show that consumers have higher performance expectancy for nonhumanoid robots. This performance expectancy generates brand forgiveness and revisit intentions for male consumers but does not affect female consumers' forgiveness and revisit behaviors.Originality/valueThis study contributes to the literature by taking a long-term perspective to investigate the outcomes after service failure, providing evidence for pending questions in previous studies and enriching studies of gender differences. Additionally, this study provides practical implications to consider the use of anthropomorphism in robots, advocate for functional confidence in robots and target consumers across genders.

Preschoolers' anthropomorphizing of robots: Do human-like properties matter?

Prior work has yielded contradicting evidence regarding the age at which children consistently and correctly categorize things as living or non-living. The present study tested children's animacy judgments about robots with a Naïve Biology task. In the Naïve Biology task, 3- and 5-year-olds were asked if robots, animals, or artifacts possessed mechanical or biological internal parts. To gauge how much children anthropomorphize robots in comparison to animals and artifacts, children also responded to a set of interview questions. To examine the role of morphology, two robots were used: a humanoid robot (Nao) and a non-humanoid robot (Dash). To investigate the role of dynamic characteristics, children saw one robot behave in a goal-directed manner (i.e., moving towards a ball) and one robot exhibit non-goal-directed behavior (i.e., moving away from a ball). Children of both age groups correctly attributed biological insides to the animal and mechanical insides to the artifact. However, 3-year-olds seemed confused about what belonged inside both robots and assigned biological and mechanical insides equally. In contrast, 5-year-olds correctly assigned mechanical insides to both robots, regardless of the robot's morphology or goal-directedness. Regarding the Animacy Interview, 3-year-olds performed at chance level when asked about the animacy of robots, animals, and artifacts. In contrast, 5-year-olds correctly attributed animacy to animals and accurately refrained from anthropomorphizing artifacts and the non-humanoid robot Dash. However, 5-year-olds performed at chance for Nao, suggesting they may be confused about the psychological properties of a human-looking robot. Taken together, these findings reveal a developmental transition during the preschool years in the attribution of biological and psychological properties to social robot.

May robots be held responsible for service failure and recovery? The role of robot service provider agents’ human-likeness

This research investigates how consumers attribute service failure and recovery responsibilities and respond to them differently based on the service provider agent type (human, humanoid, and non-humanoid robot). Two experiments show that, first, consumers attribute more service failure responsibility to the firm when the agent is less human-like. Second, they attribute more recovery responsibility to the agent and less to the firm when those agents are human, rather than robots. Third, failure (recovery) attribution to the firm reduces (enhances) consumer forgiveness and satisfaction. This study identifies the impact of human-likeness and humanness on responsibility attribution processes in interaction with robotic and human agents.

Politeness in Human–Robot Interaction: A Multi-Experiment Study with Non-Humanoid Robots

We studied politeness in human–robot interaction based on Lakoff’s politeness theory. In a series of eight studies, we manipulated three different levels of politeness of non-humanoid robots and evaluated their effects. A table-setting task was developed for two different types of robots (a robotic manipulator and a mobile robot). The studies included two different populations (old and young adults) and were conducted in two conditions (video and live). Results revealed that polite robot behavior positively affected users' perceptions of the interaction with the robots and that participants were able to differentiate between the designed politeness levels. Participants reported higher levels of enjoyment, satisfaction, and trust when they interacted with the politest behavior of the robot. A smaller number of young adults trusted the politest behavior of the robot compared to old adults. Enjoyment and trust of the interaction with the robot were higher when study participants were subjected to the live condition compared to video and participants were more satisfied when they interacted with a mobile robot compared to a manipulator.

Humanoid versus non-humanoid robots: How mortality salience shapes preference for robot services under the COVID-19 pandemic?

This paper examines the influence of mortality salience on preference for humanoid robot service. Six studies confirm that consumers/tourists are reluctant to adopt humanoid (vs. non-humanoid) service robots and robotic services when mortality is salient. The effect is driven by the perceived threat to human identity. However, temporal distance can alleviate the mortality salience effect. Eliciting a distant-future temporal perspective can reduce consumers'/tourists' existential anxiety, and then attenuate negative reactions to humanoid service robots. This research provides an innovative standpoint on consumers' reactions to service robots under conditions of mortality salience (e.g., during the COVID-19 pandemic). It also offers insight into service robot implementation and design in the hospitality and tourism industry.

Politeness in human-robot interaction: A multi-experiment study with non-humanoid robots

Politeness in human-robot interaction: A multi-experiment study with non-humanoid robots

Politeness in human-robot interaction: A multi-experiment study with non-humanoid robots

Politeness in human-robot interaction: A multi-experiment study with non-humanoid robots

How Can Autonomous Vehicles Convey Emotions to Pedestrians? A Review of Emotionally Expressive Non-Humanoid Robots

In recent years, researchers and manufacturers have started to investigate ways to enable autonomous vehicles (AVs) to interact with nearby pedestrians in compensation for the absence of human drivers. The majority of these efforts focuses on external human–machine interfaces (eHMIs), using different modalities, such as light patterns or on-road projections, to communicate the AV’s intent and awareness. In this paper, we investigate the potential role of affective interfaces to convey emotions via eHMIs. To date, little is known about the role that affective interfaces can play in supporting AV–pedestrian interaction. However, emotions have been employed in many smaller social robots, from domestic companions to outdoor aerial robots in the form of drones. To develop a foundation for affective AV–pedestrian interfaces, we reviewed the emotional expressions of non-humanoid robots in 25 articles published between 2011 and 2021. Based on findings from the review, we present a set of considerations for designing affective AV–pedestrian interfaces and highlight avenues for investigating these opportunities in future studies.

Determining Shape and Size of Personal Space of a Human when Passed by a Robot

Autonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results give us more insight into the shape and size of personal space in human–robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals.

Enhancing Emotional Support: The Effect of a Robotic Object on Human–Human Support Quality

Emotional support in the context of psychological caregiving is an important aspect of human–human interaction that can significantly increase well-being. In this study, we tested if non-verbal gestures of a non-humanoid robot can increase emotional support in a human–human interaction. Sixty-four participants were invited in pairs to take turns in disclosing a personal problem and responding in a supportive manner. In the experimental condition, the robotic object performed emphatic gestures, modeled according to the behavior of a trained therapist. In the baseline condition, the robotic object performed up-and-down gestures, without directing attention towards the participants. Findings show that the robot’s empathy-related gestures significantly improved the emotional support quality provided by one participant to another, as indicated by both subjective and objective measures. The non-humanoid robot was perceived as peripheral to the natural human–human interaction and influenced participants’ behavior without interfering. We conclude that non-humanoid gestures of a robotic object can enhance the quality of emotional support in intimate human–human interaction.

Workspace analysis and kinematics simulation of 6-DOF upper limb exoskeleton robot

In this paper, the outer non-humanoid robot 6-DOF upper limb bone as the research object, the establishment of a forward kinematics model of the robot be modeled, using the DH parameter method in Creo software to get the robot based on Monte Carlo Method Point cloud image of workspace. Based on ADAMS, the dynamic of the joint driving torque is solved, and the torque change curve of each joint during the vertical lifting of the arm is obtained. The simulation results show that the working space at the end of the six-degree-of-freedom upper-limb exoskeleton robot model can meet the working requirements, and the joint torque is relatively small, which lays the foundation for the subsequent trajectory planning and drive device design of the upper-limb exoskeleton robot.

Are friends electric? The benefits and risks of human-robot relationships.

SummarySocial robots that can interact and communicate with people are growing in popularity for use at home and in customer-service, education, and healthcare settings. Although growing evidence suggests that co-operative and emotionally aligned social robots could benefit users across the lifespan, controversy continues about the ethical implications of these devices and their potential harms. In this perspective, we explore this balance between benefit and risk through the lens of human-robot relationships. We review the definitions and purposes of social robots, explore their philosophical and psychological status, and relate research on human-human and human-animal relationships to the emerging literature on human-robot relationships. Advocating a relational rather than essentialist view, we consider the balance of benefits and harms that can arise from different types of relationship with social robots and conclude by considering the role of researchers in understanding the ethical and societal impacts of social robotics.

To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery?

Robots are the next wave in service technology; however, this advanced technology is not perfect. This research examines how social perceptions regarding the warmth and competence of service robots influence consumer reactions to service failures and recovery efforts by robots. We argue that humanoid (vs. nonhumanoid) service robots are more strongly associated with warmth (whereas competence does not differ). This tendency to expect greater warmth from humanoid robots has important consequences for service firms: (i) consumers are more dissatisfied due to lack of warmth following a process failure caused by a humanoid (vs. nonhumanoid; Study 1); (ii) humanoids (but not nonhumanoids) can recover a service failure by themselves via sincere apology, restoring perceptions of warmth (Study 2A); (iii) humanoids (but not nonhumanoids) can also effectively provide explanations as a recovery tactic (Study 2B); and, importantly, (iv) human intervention can be used to mitigate dissatisfaction following inadequate recovery by a nonhumanoid robot (Study 3), supporting the notion of human-robot collaboration. Taken together, this research offers theoretical implications for robot anthropomorphism and practical implications for firms employing service robots.

How Movements of a Non-Humanoid Robot Affect Emotional Perceptions and Trust

The way that we move often carries emotional meaning that the people with whom we interact are adept at detecting. Humans treat the movements of robots similarly, attributing the same emotions when the robots move in ways that are analogous to emotionally-charged human movements. However, this HRI work has primarily been done on humanoid or animal-shaped robots. In this paper, we examined whether this effect would hold when people observed the movements of a non-humanoid robot, Cozmo. Moreover, the attribution of emotional stance to another agent is key in the process of predicting the behavior of the other (Eivers AR et al. in Br J Dev Psychol 28:499–504, 2010). This process is laid bare in transactional scenarios where the predicted level of trust guides the humans behavior. The ultimatum game is a transactional framework that we have adapted to allow us to test in stages how humans predict and react to the behavior of the robot. We performed a study in which people played two rounds of the ultimatum game with a non-humanoid robot that moved in either a positive or negative manner. We found that in both rounds people in the Positive Movement condition rated Cozmo’s emotional valence as higher than those in the Negative Movement condition. In the second round, after Cozmo had responded to the first offers that the participants made, Cozmo’s bid response was a significant factor in the Positive Movement condition, in which participants whose first bids were rejected by Cozmo rated its emotional valence as lower than those whose bids were accepted by Cozmo. There was not an effect of movement on trust. We also ran a series of exploratory analyses to explore how various factors affected participants’ reasonings about Cozmo’s behavior, and found that unexpected, non-social behaviors (such as moving in a negative manner or rejecting a participant’s offer) lead to an increase in anthropomorphic behavior explanations.

Measuring the Perceived Social Intelligence of Robots

Robotic social intelligence is increasingly important. However, measures of human social intelligence omit basic skills, and robot-specific scales do not focus on social intelligence. We combined human robot interaction concepts of beliefs, desires, and intentions with psychology concepts of behaviors, cognitions, and emotions to create 20 Perceived Social Intelligence (PSI) Scales to comprehensively measure perceptions of robots with a wide range of embodiments and behaviors. Participants rated humanoid and non-humanoid robots interacting with people in five videos. Each scale had one factor and high internal consistency, indicating each measures a coherent construct. Scales capturing perceived social information processing skills (appearing to recognize, adapt to, and predict behaviors, cognitions, and emotions) and scales capturing perceived skills for identifying people (appearing to identify humans, individuals, and groups) correlated strongly with social competence and constituted the Mind and Behavior factors. Social presentation scales (appearing friendly, caring, helpful, trustworthy, and not rude, conceited, or hostile) relate more to Social Response to Robots Scales and Godspeed Indices, form a separate factor, and predict positive feelings about robots and wanting social interaction with them. For a comprehensive measure, researchers can use all PSI 20 scales for free. Alternatively, they can select the most relevant scales for their projects.

Fast and Intuitive Kinematics Mapping for Human-Robot Motion Imitating: A Virtual-Joint-Based Approach

Abstract It is quite difficult to imitate the motion of human arms using non-humanoid robots due to their dissimilar embodiments (degree-of-freedom, body morphology, and constraints). However, in most cases of the robotic imitation, the human operator and the robot would not share the same kinematic configuration. This paper addresses the motion imitation problem between the human arm and an industrial robot, where a commonly-used UR5 robot is considered. The motion of the human arm is obtained by an inertial motion capture system, and then the captured motion is reproduced using the UR5 embodiment. A virtual-joint-based approach is proposed to facilitate the fast and intuitive kinematics mapping between the human arm and the UR5 robot, leading to a robotic imitation system that can imitate the tip location and configuration of the human arm simultaneously. The proposed approach is verified experimentally on a real UR5 robot and compared with classic Cartesian-space-based mapping approach and joint-space-based approach.