Development Of Social Robots Research Articles

The acceptance of the potential use of social robots for children with autism spectrum disorder by Indonesian occupational therapists: a mixed methods study

Purpose Social robots have shown positive effects in treating children with autism spectrum disorder. The development of social robots in Indonesia has enabled their potential use in occupational therapy. This study aimed to investigate the factors influencing the acceptance of the potential use of social robots by Indonesian occupational therapists in clinical practice. Methods This study employed a mixed methods explanatory sequential design. An adapted unified theory of acceptance and use of technology model was utilised for the quantitative phase. The questionnaire explored the acceptance of social robots. The data were analysed using structural equation modelling. In the qualitative phase, semi-structured interviews with reflexive thematic analysis were conducted. The second phase aimed to explain the reasons behind the quantitative results and factors related to the acceptance of social robots in therapy. Results Occupational therapists showed high interest in using social robots in their sessions, as indicated by the significant positive relationship between performance expectancy and potential use. Three influential factors affecting acceptance emerged in the qualitative phase: occupational therapists’ characteristics and competencies, social robots and occupational therapy interventions, and environmental influence. Conclusions Indonesian occupational therapists have shown interest in using social robots. However, there are challenges regarding the practical application of social robots concerning individual differences in the factors that influence acceptance.

Programmed to do good: The categorical imperative as a key to moral behavior of social robots

Social robots—such as autonomous vehicles, service robots, or healthcare robots—are designed to support tasks in a broad range of human activities. However, these robots face moral dilemmas because they must make decisions that may do good for one human but potentially inflict harm on another. We argue that Kant's categorical imperative provides a framework for algorithm-based moral decision-making. By systematically addressing ethical concerns from the outset in the development of the algorithms that steer social robots, their designers can help ensure that such robots promote the well-being of individuals, communities, and society. We conclude that those involved in the development of social robots need to embed ethics into their design and functioning. The solutions to the ethical dilemmas we advance in this paper can help improve the adoption and impact of social robots. The presented insights contribute to research, practice, and policy.

Social robotics in music education: A systematic review

IntroductionSocial robotics applied to the educational context deals with proposals that start from the present to shape the future of what training in a specific subject can be. On this occasion, the aim is to investigate the connection between the utilization of social robots and the teaching–learning processes that take place within a formal music education environment at any stage of education.MethodsTo carry out this research, the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) model served as a reference to perform a systematic review of articles published in two of the most important scientific databases, Web of Science (WoS) and Scopus, since 2015.ResultsA total of four articles fulfilled the inclusion criteria.DiscussionOur findings attest that social robotics still remains a practically unknown topic. Hardly any experiences have been developed in classrooms. In general, it is considered necessary for education, and more specifically for music, not to be left out of the developments in social robotics. This technology, which is increasingly present in various areas of our society, responds to the objective of defining the 21st century, and musical education is part of it.

Recent Developments on Social Robots and Imitation Learning for Robotic Therapy

The development of social robots has been notably increasing and gaining popularity in recent times. These are also being integrated into healthcare systems, as a means to accompany patients, provide mental health therapy, and entertainment in place of direct human intervention. This paper discusses the recent developments on imitation learning for robot therapy in the field of social robotics to gain knowledge about the importance of this approach as an alternative solution to mental health therapy. The integration of robots to the mental healthcare system is known as robot therapy, which is used as a substitute for animal assisted therapy. Therapy that makes use of animals has been proven to be effective in dealing with mental disorders. However, there are risks such as allergic reactions, bites, and scratches that come with animal assisted therapy, but not robot therapy. The goal for developing robots for this is to make them seem almost life-like--has a way of thinking and emotions. For this to happen, humanoids are being programmed to appear human-like. A solution for this is imitation learning, which is a way for machines to learn, not only tasks, but responses in certain situations, only by observing and imitating humans in an environment.

Real-Time Path Planning Based on Harmonic Functions under a Proper Generalized Decomposition-Based Framework.

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

The effect of direct and extended contact on attitudes towards social robots

The development of social robots has the potential to address significant societal concerns, however, most people have limited experience of such technology. The present research investigated whether techniques borrowed from the psychology of intergroup relations – namely direct and extended contact – affect people's attitudes towards robots. Participants were provided with either direct contact with a social robot or extended contact (these participants watched a video recorded by a friend who had met the robot) before their explicit and implicit attitudes towards robots were measured. Results indicated that direct contact affected both explicit and implicit attitudes, while extended contact affected implicit attitudes. The implication of these findings is that contact with a robot, direct or indirect, can change attitudes; much as previous research has shown that contact with a person who is a member of an out-group can change attitudes towards that group. We conclude that methods and theories from the study of human intergroup relationships can be usefully applied to understand attitudes toward social robots.

Towards a new scale for assessing attitudes towards social robots

AbstractBackground:The surge in the development of social robots gives rise to an increased need for systematic methods of assessing attitudes towards robots.Aim:This study presents the development of a questionnaire for assessing attitudinal stance towards social robots: the ASOR.Methods:The 37-item ASOR questionnaire was developed by a task-force with members from different disciplines. It was founded on theoretical considerations of how social robots could influence five different aspects of relatedness.Results:Three hundred thirty-nine people responded to the survey. Factor analysis of the ASOR yielded a three-factor solution consisting of a total of 25 items: “ascription of mental capacities”, “ascription of socio-practical capacities”, and “ascription of socio-moral status”. This data was further triangulated with data from interviews (n = 10).Conclusion:the ASOR allows for assessment of three distinct facets of ascription of capacities to social robots and offers a new type of assessment of attitudes towards social robots. It appeared that ASOR not only assesses ascription of capacities to social robots but it also gauged overall positive attitudes towards social robots.

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots’ acceptance. Implications for the development of persuasive robots are discussed.

“I’ll take care of you,” said the robot

AbstractThe insertion of robotic and artificial intelligent (AI) systems in therapeutic settings is accelerating. In this paper, we investigate the legal and ethical challenges of the growing inclusion of social robots in therapy. Typical examples of such systems are Kaspar, Hookie, Pleo, Tito, Robota,Nao, Leka or Keepon. Although recent studies support the adoption of robotic technologies for therapy and education, these technological developments interact socially with children, elderly or disabled, and may raise concerns that range from physical to cognitive safety, including data protection. Research in other fields also suggests that technology has a profound and alerting impact on us and our human nature. This article brings all these findings into the debate on whether the adoption of therapeutic AI and robot technologies are adequate, not only to raise awareness of the possible impacts of this technology but also to help steer the development and use of AI and robot technologies in therapeutic settings in the appropriate direction. Our contribution seeks to provide a thoughtful analysis of some issues concerning the use and development of social robots in therapy, in the hope that this can inform the policy debate and set the scene for further research.

Influence of Embodiment and Substrate of Social Robots on Users’ Decision-Making and Attitude

We explored decision-making and attitudes from humans toward robots with different embodiments and substrates. Specifically, four types of robots were compared through experiments, namely, a virtual reality robot, an augmented reality robot, a physical robot, and a physical but tele-present robot that was displayed on a screen. For the experiments, 60 participants were divided into four groups to answer ten questions, and the robots provided advice about each question. Then, the participants chose whether to take the advice and their initial and final choices were registered. We measured each participant’s faith, attachment, social presence, and credibility toward the four robots through questionnaires. We found that the physical embodiment is significantly more favored by the participants, whereas the virtual embodiment is the less favored. Moreover, a robot sharing the same substrate as the participant is preferred over one not sharing the same substrate. These findings, their interpretation, and application to robots can lead to improved design and development of social robots.

Developing social robots for aging populations: A literature review of recent academic sources

AbstractThe perception of aging populations is a major factor driving the social robot development movement. A growing body of research reflects the expanding interest in social robots. This paper synthesizes research on the development of social robots with a literature review of academic articles with publication dates ranging from 2006 to 2017. The review is divided into three themes: (a) robots as an aid in treatment; (b) robots as social assistants and home companions; and (c) robots as custodial caregivers that are viewed in terms of ethical implications. This paper outlines the issues surrounding social, commitment, assistive, and companion robots for use in medical treatment, mental health therapy, physiotherapy, care facilities, and private homes. It describes some of the ethical concerns raised by researchers and media, including questions of control, privacy, consent, and the issue of simulated versus human compassion in caregiving. The research also points out that a rhetoric of urgency concerning aging populations drives the development of robots, which frames citizens who will benefit from robots in reductive ways. We argue that the contribution of humanities and social science research, including age studies and critical gerontology, should be better integrated with discourses of social robot development, largely from technical fields.

Whimsical Bodies and Performative Machines: Aesthetics and Affects of Robotic Art

Abstract This article explores the ways in which robots’ behaviours are designed and curated to elicit reactions from their human counterparts. Through the work of artists such as Nam June Paik, Steve Daniels, Edward Ihnatowicz and Norman White, a survey of robotic art illustrates a particular aesthetic and behavioural language that is non-threatening, animalistic, cute, quaint and whimsical. Considering the artists’ programming of behaviours and construction of aesthetics, the use of animal behavioural modelling, and developments in social robotics, this article unpacks how meaning is inscribed onto robots and in return how affect is transmitted to human viewers. By exploring the whimsical bodies, performative machines and networked nonhumans brought forth in robotic artworks, this article draws out how aesthetic and behavioural languages of robotic art play into peoples’ emotional and affective encounters with them.

A Model for Facial Emotion Inference Based on Planar Dynamic Emotional Surfaces

Emotions have direct influence on the human life and are of great importance in relationships and in the way interactions between individuals develop. Because of this, they are also important for the development of human-machine interfaces that aim to maintain a natural and friendly interaction with its users. In the development of social robots, which this work aims for, a suitable interpretation of the emotional state of the person interacting with the social robot is indispensable. The focus of this paper is the development of a mathematical model for recognizing emotional facial expressions in a sequence of frames. Firstly, a face tracker algorithm is used to find and keep track of faces in images; then the found faces are fed into the model developed in this work, which consists of an instantaneous emotional expression classifier, a Kalman filter and a dynamic classifier that gives the final output of the model.

Expressing Emotions in Social Robotics - A Schematic Overview Concerning the Mechatronics Aspects and Design Concepts

Facial features play an important role in expressing emotional state. Therefore, rendering realistic facial expressions plays an important role in the development of social robots within HRI. Hence, the design solutions must meet these requirements. Often mechatronic and design solutions poorly chosen can lead to unsatisfactory results. In this paper, a schematic overview concerning the design solutions adopted in the development of social robots is presented.

Correlates of facial expressions in the primary visual cortex

Face detection and recognition should be complemented by recognition of facial expression, for example for social robots which must react to human emotions. Our framework is based on two multi-scale representations in cortical area V1: keypoints at eyes, nose and mouth are grouped for face detection [1]; lines and edges provide information for face recognition [2]. We assume that keypoints play a key role in the where system, lines and edges being exploited in the what system. This dichotomy, together with coarse-to-fine-scale processing, yields translation and rotation invariance, refining object categorisations until recognition, assuming that objects are represented by normalised templates in memory. Faces are processed the following way: (1) Keypoints at coarse scales are used to translate and rotate the entire input face, using a generic face template with neutral expression. (2) At medium scales, cells with dendritic fields at corners of mouth and eyebrows of the generic template collect evidence for expressions using the line and edge information of the (globally normalised) input face at those scales. Big structures, including mouth and eyebrows, are further normalised using keypoints and first categorizations (gender, race) are obtained using lines and edges. (3) The latter process continues until the finest scale, with normalisation of the expression to neutral for final face recognition. The advantage of this framework is that only one frontal view of a person's face with neutral expression must be stored in memory. This procedure resulted from an analysis of the multi-scale line/edge representation of normalised faces with seven expressions: neutral, anger, disgust, fear, happy, sad and surprise. Following [3], where Action Units (AUs) are related to facial muscles, we analysed the line/edge representation in all AUs. We found that positions of lines and edges at one medium scale, and only at AUs covering the mouth and eyebrows, relative to positions in the neutral face at the same scale, suffice to extract the right expression. Moreover, by implementing AUs by means of six groups of vertically aligned summation cells with a dendritic field size related to that scale (sizes of simple and complex cells), covering a range of positions above and below the corners of mouth and eyebrows in the neutral face, the summation cell with maximum response of each of the six cell groups can be detected, and it is possible to estimate the degree of the expression, from mild to extreme. This work is in progress, since the method must still be tested using big databases with many faces and their natural variations. Perhaps some expressions detected at one medium scale must be validated at one or more finer scales. Nevertheless, in this framework detection of expression occurs before face recognition, which may be an advantage in the development of social robots.