A Survey of Expectations About the Role of Robots in Robot-Assisted Therapy for Children with ASD: Ethical Acceptability,Trust, Sociability,Appearance, and Attachment

The use of robots in therapy for children with autism spectrum disorder (ASD) raises issues concerning the ethical and social acceptability of this technology and, more generally, about human–robot interaction. However, usually philosophical papers on the ethics of human–robot-interaction do not take into account stakeholders’ views; yet it is important to involve stakeholders in order to render the research responsive to concerns within the autism and autism therapy community. To support responsible research and innovation in this field, this paper identifies a range of ethical, social and therapeutic concerns, and presents and discusses the results of an exploratory survey that investigated these issues and explored stakeholders’ expectations about this kind of therapy. We conclude that although in general stakeholders approve of using robots in therapy for children with ASD, it is wise to avoid replacing therapists by robots and to develop and use robots that have what we call supervised autonomy. This is likely to create more trust among stakeholders and improve the quality of the therapy. Moreover, our research suggests that issues concerning the appearance of the robot need to be adequately dealt with by the researchers and therapists. For instance, our survey suggests that zoomorphic robots may be less problematic than robots that look too much like humans.     

Robotic Nudges: The Ethics of Engineering a More Socially Just Human Being

Robots are becoming an increasingly pervasive feature of our personal lives. As a result, there is growing importance placed on examining what constitutes appropriate behavior when they interact with human beings. In this paper, we discuss whether companion robots should be permitted to “nudge” their human users in the direction of being “more ethical”. More specifically, we use Rawlsian principles of justice to illustrate how robots might nurture “socially just” tendencies in their human counterparts. Designing technological artifacts in such a way to influence human behavior is already well-established but merely because the practice is commonplace does not necessarily resolve the ethical issues associated with its implementation.     

The TROCA Project: An autonomous transportation robot controlled by a cognitive architecture

Autonomous mobile robots emerged as an important kind of transportation system in warehouses and factories. In this work, we present the use of MECA cognitive architecture in the development of an artificial mind for an autonomous robot responsible for multiple tasks, including transportation of packages along a factory floor, environment exploration, warehouse inventory, its internal energy management, self-monitoring and dealing with human operators and other robots. The present text provides a detailed specification for the architecture and its software implementation. Future work will present the simulation results under different configurations, together with a detailed analysis of the architecture performance and its generalization for autonomous robot control.     

The CORTEX cognitive robotics architecture: Use cases

CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in real world, human interacting robots, by selecting an a priori functional decomposition of the capabilities of the robot. This set of abilities was then translated to computational modules or agents, each one built as a network of software interconnected components. The nature of these agents can range from pure reactive modules connected to sensors and/or actuators, to pure deliberative ones, but they can only communicate with each other through a graph structure called Deep State Representation (DSR). DSR is a short-term dynamic representation of the space surrounding the robot, the objects and the humans in it, and the robot itself. All these entities are perceived and transformed into different levels of abstraction, ranging from geometric data to high-level symbolic relations such as “the person is talking and gazing at me”. The combination of symbolic and geometric information endows the architecture with the potential to simulate and anticipate the outcome of the actions executed by the robot. In this paper we present recent advances in the CORTEX architecture and several real-world human-robot interaction scenarios in which they have been tested. We describe our interpretation of the ideas inspiring the architecture and the reasons why this specific computational framework is a promising architecture for the social robots of tomorrow.     

Can we talk to robots? Ten-month-old infants expected interactive humanoid robots to be talked to by persons

As technology advances, many human-like robots are being developed. Although these humanoid robots should be classified as objects, they share many properties with human beings. This raises the question of how infants classify them. Based on the looking-time paradigm used by [Legerstee, M., Barna, J., & DiAdamo, C., (2000). Precursors to the development of intention at 6 months: understanding people and their actions. Developmental Psychology, 36, 5, 627-634.], we investigated whether 10-month-old infants expected people to talk to a humanoid robot. In a familiarization period, each infant observed an actor and an interactive robot behaving like a human, a non-interactive robot remaining stationary, and a non-interactive robot behaving like a human. In subsequent test trials, the infants were shown another actor talking to the robot and to the actor. We found that infants who had previously observed the interactive robot showed no difference in looking-time between the two types of test events. Infants in the other conditions, however, looked longer at the test event where the second experimenter talked to the robot rather than where the second experimenter talked to the person. These results suggest that infants interpret the interactive robot as a communicative agent and the non-interactive robot as an object. Our findings imply that infants categorize interactive humanoid robots as a kind of human being.     

Effects of service robots' anthropomorphism on consumers' attribution toward and forgiveness of service failure

We investigated the effects of service robots' anthropomorphism on consumers' attribution and forgiveness of service failure. By manipulating the levels of anthropomorphism in service robots in three experiments, we evaluated the relationship norms and involvement levels of consumers. Three contributions of our study are as follows. First, most human–robot interaction studies have focused on consumers' dissatisfaction with service robots' service failures. Unfortunately, few studies have investigated the influence of service robots' anthropomorphism levels on consumers' internal attribution and forgiveness of service failure. In the present study, we found a positive correlation between the former and the latter. The findings of this study indicate marketing managers should design their service robots to be less anthropomorphic to reduce the likelihood of consumers making an internal attribution when a service failure occurs and increase their forgiveness of service failure. In addition, to reduce the likelihood of consumers' internal attribution upon service failure, managers may ensure that their service robots explain to the consumers the cause of service failure (e.g., inability to understand consumers' requirements and robots' design-related limitations). Second, consumers' relationship norms moderate the effect of anthropomorphism level on internal attribution. Third, robot anthropomorphism is reportedly effective only when anthropomorphization occurs subconsciously.     

Toward ethical cognitive architectures for the development of artificial moral agents

New technologies based on artificial agents promise to change the next generation of autonomous systems and therefore our interaction with them. Systems based on artificial agents such as self-driving cars and social robots are examples of this technology that is seeking to improve the quality of people’s life. Cognitive architectures aim to create some of the most challenging artificial agents commonly known as bio-inspired cognitive agents. This type of artificial agent seeks to embody human-like intelligence in order to operate and solve problems in the real world as humans do. Moreover, some cognitive architectures such as Soar, LIDA, ACT-R, and iCub try to be fundamental architectures for the Artificial General Intelligence model of human cognition. Therefore, researchers in the machine ethics field face ethical questions related to what mechanisms an artificial agent must have for making moral decisions in order to ensure that their actions are always ethically right. This paper aims to identify some challenges that researchers need to solve in order to create ethical cognitive architectures. These cognitive architectures are characterized by the capacity to endow artificial agents with appropriate mechanisms to exhibit explicit ethical behavior. Additionally, we offer some reasons to develop ethical cognitive architectures. We hope that this study can be useful to guide future research on ethical cognitive architectures.     

When AI meets PC: exploring the implications of workplace social robots and a human-robot psychological contract

ABSTRACT

The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely future psychological contract partners for human employees, given these entities transform notions of workplace technology from being a tool to being an active partner. We first overview the increasing role of robots in the workplace, particularly through the advent of sociable AI, and synthesize the literature on human–robot interaction. We then develop an account of a human-social robot psychological contract and zoom in on the implications of this exchange for the enactment of reciprocity. Given the future-focused nature of our work we utilize a thought experiment, a commonly used form of conceptual and mental model reasoning, to expand on our theorizing. We then outline potential implications of human-social robot psychological contracts and offer a range of pathways for future research.     

Assembling human empathy towards care robots: The human labor of robot sociality

This paper conceptualizes human to robot empathy as empathetic arrangements configured in caring spaces. Analyzing empathy towards care robots as arrangements comprising robots, spaces, discourses, bodies and institutions enables recognition of the way empathy is about self-other relationships while eschewing an understanding of empathy in terms of a reciprocal relationship between human and robot. Situating the therapeutic, zoomorphic robot, Paro and the health care support robot, Care-O-Bot as part of empathetic arrangements draws attention to how the cultivation of empathy towards robots governs and regulates patient sociality. In particular, it shows that these robots do not function as substitutes for human carers but instead are dependent on human labor if they are to deliver therapy ethically and effectively. They rely on the affective labor of the patient and the labor of carers and others in the arrangement.     

The Intervention of Robot Caregivers and the Cultivation of Children’s Capability to Play

In this article, the authors examine whether and how robot caregivers can contribute to the welfare of children with various cognitive and physical impairments by expanding recreational opportunities for these children. The capabilities approach is used as a basis for informing the relevant discussion. Though important in its own right, having the opportunity to play is essential to the development of other capabilities central to human flourishing. Drawing from empirical studies, the authors show that the use of various types of robots has already helped some children with impairments. Recognizing the potential ethical pitfalls of robot caregiver intervention, however, the authors examine these concerns and conclude that an appropriately designed robot caregiver has the potential to contribute positively to the development of the capability to play while also enhancing the ability of human caregivers to understand and interact with care recipients.     

Using robots at work during the COVID-19 crisis evokes passion decay: Evidence from field and experimental studies

The growing trend of introducing robots into employees' work lives has become increasingly salient during the global COVID-19 pandemic. In light of this pandemic, it is likely that organisational decision-makers are seeing value in coupling employees with robots for both efficiency- and health-related reasons. An unintended consequence of this coupling, however, may be an increased level of work routinisation and standardisation. We draw primarily from the model of passion decay from the relationship and clinical psychology literature to develop theory and test a model arguing that passion decays as employees increasingly interact with robots for their work activities. We demonstrate that this passion decay leads to an increase of withdrawal behaviour from both the domains of work and family. Drawing further from the model of passion decay, we reveal that employees higher in openness to experience are less likely to suffer from passion decay upon more frequent interactions with robots in the course of work. Across a multi-source, multi-wave field study conducted in Hong Kong (Study 1) and a simulation-based experiment conducted in the United States (Study 2), our hypotheses received support. Theoretical and practical implications are discussed.     

Model learning for robot control: a survey

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.     

Education biases perception of social robots

IntroductionRecent research on human–robot interactions (HRI) emphasizes a role of user's attitudes in perceiving robot's with different robot embodiments of varying levels of human likenesses. However, other human factors such as educational background may also help understanding of what conditions contribute to enhance social perception of robot's features.ObjectivesThis study aimed to determine how people's attitudes towards and familiarization with robots influence social perception of particular features of robots.MethodFirst, we measured attitudes towards robots among undergraduate students with diverse educational background (engineering vs. psychology). Then, participants were presented with short movies showing the behaviour of three robots with different levels of sociability. Finally, participants evaluated the characteristics of these robots on a scale.ResultsPeople more familiar with social robots and with more positive attitudes towards them evaluate robots with human traits more highly.ConclusionHuman perception of social robots resembles social phenomena related to human perception of other people.     

Robots make no effort! Service evaluation and consumer mindset

Considering the growing acceptance of humanoid robots in the service industry, this study aimed to examine their negative impact on service evaluation, as well as the underlying mechanism of perceived effort and the moderating role of consumer mindset. Three experiments that used different service scenarios revealed that humanoid service robots negatively affected service evaluation compared to human employees, and this effect was mediated by decreased perceived effort. Furthermore, this negative impact was attenuated when consumers had a concrete mindset compared to abstract. This work contributes to both consumer service and robot literature by elaborating on the possible adverse influence of replacing human employees with humanoid service robots. It also offers managerial implications for how and when to adopt a robot service in this machine age.     

Populations of spiking neurons for reservoir computing: Closed loop control of a compliant quadruped

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing framework. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.     

“Danger,Will Robinson!” The challenges of social robots for intergroup relations

Society's increasing reliance on robots in everyday life provides exciting opportunities for social psychologists to work with engineers in the nascent field of social robotics. In contrast to industrial robots that, for example, may be used on an assembly line, social robots are designed specifically to interact with humans and/or other robots. People tend to perceive social robots as autonomous and capable of having a mind. As such, they are also more likely to be subject to social categorization by humans. As social robots become more human like, people may also feel greater empathy for them and treat robots more like (human) ingroup members. On the other hand, as they become more human like, robots also challenge our human distinctiveness, threaten our identity, and elicit suspicion about their ability to deceive us with their human‐like qualities. We review relevant research to explore this apparent paradox, particularly from an intergroup relations perspective. We discuss these findings and propose three research questions that we believe social psychologists are ideally suited to address.     

The Robot in the Crib: A Developmental Analysis of Imitation Skills in Infants and Robots

Interesting systems, whether biological or artificial, develop. Starting from some initial conditions, they respond to environmental changes, and continuously improve their capabilities. Developmental psychologists have dedicated significant effort to studying the developmental progression of infant imitation skills, because imitation underlies the infant's ability to understand and learn from his or her social environment. In a converging intellectual endeavour, roboticists have been equipping robots with the ability to observe and imitate human actions because such abilities can lead to rapid teaching of robots to perform tasks. We provide here a comparative analysis between studies of infants imitating and learning from human demonstrators, and computational experiments aimed at equipping a robot with such abilities. We will compare the research across the following two dimensions: (a) initial conditions-what is innate in infants, and what functionality is initially given to robots, and (b) developmental mechanisms-how does the performance of infants improve over time, and what mechanisms are given to robots to achieve equivalent behaviour. Both developmental science and robotics are critically concerned with: (a) how their systems can and do go 'beyond the stimulus' given during the demonstration, and (b) how the internal models used in this process are acquired during the lifetime of the system.