Volume 10, Issue 1

Prevalence of Autism Spectrum Disorder (ASD) in children has been increasing over the years. These children, when compared to typically growing children, face challenges in leading a day-to-day life. Other than the social interactions, communication and cognitive skills, psychomotor skill deficits are also found in them. This paper focuses on enhancement of psychomotor skills in children with ASD by employing a mechatronic training kit. Training using this kit aims at developing skills like ‘palmar grasp’, ‘wrist rotation’ and ‘eye-hand coordination’. Trials were conducted with six children with autism aged between four and nine years. Significant improvements were seen in these children both in skills and in behaviour after the training. Thus, teaching psychomotor skills to children with autism using the newly developed mechatronic door training kit is found to be working and the results are encouraging.

The 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.

Due to their predictability, controllability, and simple social abilities, robots are starting to be used in diverse ways to assist individuals with Autism Spectrum Disorder (ASD). In this work, we investigate an alternative and novel research direction for using robots in relation to ASD, through programming a humanoid robot to exhibit behaviors similar to those observed in children with ASD. We designed 16 ‘autism-like’ behaviors of different severities on a NAO robot, based on ADOS-2, the gold standard for ASD diagnosis. Our behaviors span four dimensions, verbal and non-verbal, and correspond to a spectrum of typical ASD responses to 3 different stimulus families inspired by standard diagnostic tasks. We integrated these behaviors in an autonomous agent running on the robot, with which humans can continuously interact through predefined stimuli. Through user-controllable features, we allow for 256 unique customizations of the robot’s behavioral profile.We evaluated the validity of our interactive robot both in video-based and ‘in situ’ studies with 3 therapists. We also present subjective evaluations on the potential benefits of such robots to complement existing therapist training, as well as to enable novel tasks for ASD therapy.

This article describes ethical issues related to the design and use of social robots in sensitive contexts like psychological interventions and provides insights from one user design study and two controlled experiments with adults and children. User expectations regarding privacy with a therapeutic robotic dog, Therabot, gathered from a 16 participant design study are presented. Furthermore, results from 142 forensic interviews about bullying experiences conducted with children (ages 8 to 17) using three different social robots (Nao, Female RoboKind, Male RoboKind) and humans (female and male) as forensic interviewers are examined to provide insights into child beliefs about privacy and social judgment in sensitive interactions with social robots. The data collected indicates that adult participants felt a therapeutic robotic dog would be most useful for children in comparison to other age groups, and should include privacy safeguards. Data obtained from children after a forensic interview about their bullying experiences shows that they perceive social robots as providing significantly more socially protective factors than adult humans. These findings provide insight into how children perceive social robots and illustrate the need for careful considerationwhen designing social robots that will be used in sensitive contexts with vulnerable users like children.

As healthcare turns its focus to preventative community-based interventions, there is increasing interest in using in-home technology to support this goal. This study evaluates the design and use of socially assistive robots (SARs) and sensors as in-home therapeutic support for older adults with depression. The seal-like SAR Paro, along with onboard and wearable sensors, was placed in the homes of 10 older adults diagnosed with clinical depression for one month. Design workshops were conducted before and after the in-home implementation with participating older adults and clinical care staff members. Workshops showed older adults and clinicians sawseveral potential uses for robots and sensors to support in-home depression care. Long-term in-home use of the robot allowed researchers and participants to situate desired robot features in specific practices and experiences of daily life, and some user requests for functionality changed due to extended use. Sensor data showed that participants’ attitudes toward and intention to use the robot were strongly correlated with particular Circadian patterns (afternoon and evening) of robot use. Sensor data also showed that those without pets interacted with Paro significantly more than those with pets, and survey data showed they had more positive attitudes toward the SAR. Companionship, while a desired capability, emerged as insufficient to engage many older adults in long-term use of SARs in their home.

This paper presents a patient-centered interaction design of Robot-Assisted Therapy or Robot-Assisted Play (RAP) applications for children with a diverse form of Autism Spectrum Disorder (ASD) combined with Attention Deficit Hyperactivity Disorder (ADHD). Through our iterative evaluation process based on observations of fourteen children over several sessions and interviews with their parents, we continuously refined the behaviors of a humanoid robot NAO that are now ready to be used for research on Robot-Assisted Therapy. This paper also reports the design of RAP methodology that might be helpful in assimilating iterative input from stakeholders such as children, parents and therapists to customize technology for other populations with special needs.

This article focuses on ethical issues raised by increasing levels of autonomy for surgical robots. These ethical issues are explored mainly by reference to state-ofart case studies and imminent advances in Minimally Invasive Surgery (MIS) and Microsurgery. In both area, surgicalworkspace is limited and the required precision is high. For this reason, increasing levels of robotic autonomy can make a significant difference there, and ethically justified control sharing between humans and robots must be introduced. In particular, from a responsibility and accountability perspective suitable policies for theMeaningfulHuman Control (MHC) of increasingly autonomous surgical robots are proposed. It is highlighted how MHC should be modulated in accordance with various levels of autonomy for MIS and Microsurgery robots. Moreover, finer MHC distinctions are introduced to deal with contextual conditions concerning e.g. soft or rigid anatomical environments.

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for “Good”. This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. Butwhat is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfallswhen determining, from an AI point of view,what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of “AI for Social Good”, more specifically “Data Science for Social Good”. Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: “attacks” as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.

The article presents the results of some research conducted in Italy on the perception of robots. The study was carried out as part of a project arranged by the School of Robotics ( Scuola di Robotica ) in 2017/2018 and involved around 700 Italian citizens belonging to different social groups. Data collection used mixed methods: surveys, focus groups, interviews, and participatory workshops. The results were compared with surveys conducted in Europe by the Eurobarometer. Italy is an interesting context for this type of study for the density of robots, for the growth trends of robotics compared to other countries and for the relationships between industry and the labor market. The results of the research show that the attitudes and expectations of Italians towards robots are complex, multidimensional and contradictory: on the one hand, the interviewees positively value the growing presence of robots, on the other, they show fears about the spread of robotics in society. Robots, especially those designed to interact with humans, are part of a large system influenced by social, cultural and environmental factors in which actors move with different expectations and needs. The presence of a significant degree of differentiation between the responses of the different socio-demographic categories confirms the importance of investigating these issues, in order to reduce the distance that persists between researchers and scientists and the impact of technology on society. Furthermore, robotics is often narrated with myths and legends that have no correspondence in reality; since robots will interact more fully with humans, the human-robot interaction process and the bias integrated into the technology must be carefully studied to avoid the risks of confirmatory bias.

For human ethics, it can convincingly be argued that justice is a central cornerstone and basis. Here, it is suggested that this can, to some extent, similarly be applied to robots. The article makes the argument that Rawls’ veil of ignorance in his conception of justice as fairness can effectively be replaced by a much more natural condition of prudent egoism in a finite world. Observing ones’ own important interests in an encompassing context paves the way for a guideline for the conduct, which is binding for humans, robots and each and every pragmatic agent with a minimum level of rationality. These arguments do not see humans (forever) in any privileged position: any agent, single human, state, alien or artificial with a certain minimum of general cognitive (and effective) capabilities is bound by a universal negative imperative. This entails that precautious procedures are preferable, and some general prudently constrained flexibility is required for self-consistency and survival.

In this paper, by turning to examples of visual inscriptions adopted within HRI research, and more specifically social robotic interaction, I wish to explore through visual ethnography some of the challenges faced in designing ethical robots. Firstly, visual ethnography allows for an alternative categorisation of the inscriptions of HRI based on visual characteristics. Secondly, visual inscriptions show multiple and paradoxical meanings when appearing juxtaposed revealing challenges of diminished and asymmetric consideration of human orientated concerns in favour of technical and experimental certainty. Thirdly, by taking a human orientated perspective of experimental arrangements, the understanding of ethics becomes a way of framing and of looking at inscriptions. The paper calls for a better understanding of the role of inscription practices in HRI generally in order to find new approaches useful to bolster a more robust inclusion of ethics within the field.

As more and more robots enter our social world, there is a strong need for further field studies of humanrobot interaction. Based on a two-year ethnographic study of the implementation of a South Korean socially assistive robot in Danish elderly care, this paper argues that empirical and ethnographic studies will enhance the understanding of the adaptation of robots in real-life settings. Furthermore, the paper emphasizes how users and the context of use matters to this adaptation, as it is shown that roboticists are unable to control how their designs are implemented and how the sociality of social robots is inscribed by its users in practice. This paper can be seen as a contribution to long-term studies of HRI. It presents the challenges of robot adaptation in practice and discusses the limitations of the present conceptual understanding of human-robot relations. The ethnographic data presented herein encourage a move away from static and linear descriptions of the implementation process toward more contextual and relational accounts of HRI.

The media and political-managerial levels focus on the opportunities to re-perform the Scandinavian welfare states through digitization. Especially in Denmark, this trend is prominent. Welfare technology is a Scandinavian notion used to point at assistive technologies intending to support the elderly, the disabled and care providers. Feeding assistive robotics (FAR) is a welfare technology relevant to citizens with no or low function in their arms. Despite national dissemination strategies, it proves difficult to recruit suitable users. There have been many promises for the potential of assistive robotics including more cost-efficient healthcare delivery, engaged patients and connected care providers. However, the realities of enacting assistive robotics, whether as patients or care providers, can be complicated in ways often unanticipated by government agencies and technology developers. This study discusses governmental agencies’ and technology developers’ visions with regard to what robotics may do and argues that these visions intertwine with affected stakeholders’ organizing of theirworlds. On this founding, the article discusses the resulting tinkering during implementation. The study exemplifies and demonstrates how ethnography can be used as an important method in Human Robot Interaction (HRI) research. The Actor Network Theory idea of ‘follow the actor’ inspired the study that took place as multi-sited ethnography at different locations in Denmark and Sweden. Based on desk research, observation of meals and interviews the study examines sociotechnical imaginaries and their practical and ethical implications.Human and FAR interaction demands engagement, sustained patience and understanding of the citizen’s particular body, identity and situation. The article contributes to the HRI literature by providing detailed empirical analysis based on an ethnographic studywhere political strategies, technology developers’ assumptions and affected stakeholders’ everyday hassles are in focus at the same time.

This article discusses the process of developing robot use cases using large-scale ethnographic observation as a starting point. In particular, during 296 hours of ethnographic observation of the workflows at seventeen departments at Odense University Hospital, 607 processes were described and subsequently annotated. The ethnographic method provided rich, contextually situated data that can be searched and categorized for use case development, which is illustrated on an example use case, describing the process and illustrating the type of data elicited, discussing the problems encountered and providing downloadable tools for other researchers interested in similar approaches to use case development.

This paper centers on a five-month ethnographic field study among engineers in a Danish collaborative industrial robotics project, to examine how the everyday work of engineers intersects with existing, formally-adopted engineering ethics approaches.Methods included a literature review of engineering ethics, participant observation in a technical research institute and in machine workshops, document and visual media analysis, object elicitation, and qualitative interviews. Empirical findings from this investigation are used to evaluate existing formalized engineering ethics in relation to engineering praxis. Juxtaposed with engineers’ everyday ethical decision-making practices, professional ethics approaches are shown to be based in deontological and virtue ethics, narrowly focused on the individual engineer as a professional, and thus inappropriate and insufficient for the very practical field of engineering. The author argues for an alternative direction toward a situated pragmatic and social ethics in engineering that disrupts the current social arrangement around robot development through ethnographic intervention in the engineers’ negotiation of values in the design process.

This article argues that a multi-variation approach can be a useful supplement to existing ethnographic studies in the field of Human-Robot Interaction (HRI). The multi-variation approach builds on classical ethnographic case studies, where a researcher studies a delimited field in a microstudy of a particular robot, its makers, users, and affected stakeholders. The approach is also inspired by multi-sited studies, where researchers move across fields, adding to the complexity of the ethnographic findings. Whereas both approaches build on analysis of microstudies, the multi-variation approach is further inspired by postphenomenology, where the main aim is to deliberately seek variation – thus again adding to the complexity of the detailed findings. Here, the multivariation approach includes several researchers studying several types of robots across sites. The analytical approach seeks patterns across this complexity – and the claim is that a multi-variation approach has a strength in findings that are systematic and consistent across cases, sites, and variations. The article gives an example of such cross-variation findings in the robot field – namely the tendency for roboticists across cases and robot types to publicly present their robots as more finished and wellfunctioning than they actually are.

Originating from anthropology, ethnographic reflexivity refers to ethnographers’ understanding and articulation of their own intervention to participants’ activities as innate study opportunities which affect quality of the ethnographic data. Despite of its methodological discordance with scientific methods which minimize researchers’ effects on the data, validity and effectiveness of reflexive ethnography have newly been claimed in technology studies. Inspired by the shift, I suggest potential ways of incorporating ethnographic reflexivity into studies of human-robot social interaction including ethnographic participant observation, collaborative autoethnography and hybrid autoethnography. I presume such approaches would facilitate roboticists’ access to human conditions where robots’ daily operation occurs. A primary aim here is to fill the field’s current methodological gap between needs for better-examining robots’ social functioning and a lack of insights from ethnography, prominent socio-technical methods. Supplementary goals are to yield a nuanced understanding of ethnography in HRI and to suggest embracement of reflexive ethnographies for future innovations.

In social interactions, human movement is a rich source of information for all those who take part in the collaboration. In fact, a variety of intuitive messages are communicated through motion and continuously inform the partners about the future unfolding of the actions. A similar exchange of implicit information could support movement coordination in the context of Human-Robot Interaction. In this work, we investigate how implicit signaling in an interaction with a humanoid robot can lead to emergent coordination in the form of automatic speed adaptation. In particular, we assess whether different cultures – specifically Japanese and Italian – have a different impact on motor resonance and synchronization in HRI. Japanese people show a higher general acceptance toward robots when compared with Western cultures. Since acceptance, or better affiliation, is tightly connected to imitation and mimicry, we hypothesize a higher degree of speed imitation for Japanese participants when compared to Italians. In the experimental studies undertaken both in Japan and Italy, we observe that cultural differences do not impact on the natural predisposition of subjects to adapt to the robot.

Previous studies conducted with the humanoid robot Kaspar in the UK have yielded many encouraging results. This paper examines the influence of conducting play sessions with Kaspar on the social and communication skills of children diagnosed with Autism Spectrum Condition (ASC) and suggests possible ways for using the robot as a (therapeutic) tool in a Greek school for children with special needs. Over a period of 10 weeks 7 children took part in a total of 111 individual play sessions with the Kaspar robot. Each child participated in between 12 and 18 sessions with the robot. The results from this study indicate that the play sessions with Kaspar appear to have positively influenced the behaviours of some of the children in specific domains such as communication and interaction, prompted speech, unprompted imitation and focus/attention. Furthermore, the children’s teachers expressed positive views regarding the impact of the play sessions on the children and offered interesting suggestions about the ways in which the robot could potentially be used in everyday teaching tasks and were eager to obtain a Kaspar for their classroom activities.

Recent advances in robotics allow for collaboration between humans and machines in performing tasks at home or in industrial settings without harming the life of the user. While humans can easily adapt to each other and work in team, it is not as trivial for robots. In their case, interaction skills typically come at the cost of extensive programming and teaching. Besides, understanding the semantics of a task is necessary to work efficiently and react to changes in the task execution process. As a result, in order to achieve seamless collaboration, appropriate reasoning, learning skills and interaction capabilities are needed. For us humans, a cornerstone of our communication is language that we use to teach, coordinate and communicate. In this paper we thus propose a system allowing (i) to teach new action semantics based on the already available knowledge and (ii) to use natural language communication to resolve ambiguities that could arise while giving commands to the robot. Reasoning then allows new skills to be performed either autonomously or in collaboration with a human. Teaching occurs through a web application and motions are learned with physical demonstration of the robotic arm. We demonstrate the utility of our system in two scenarios and reflect upon the challenges that it introduces.

Live performance is a vehicle where theatrical devices are used to exemplify, probe, or question how humans think about objects, each other, and themselves. This paper presents work using this vehicle to explore human perceptions of robot and human capabilities. The paper documents four performances at three distinct venues where user studies were conducted in parallel to live performance. A set of best practices for successful collection of data in this manner over the course of these trials is developed. Then, results of the studies are presented, giving insight into human opinions of a variety of natural and artificial systems. In particular, participants are asked to rate the expressivity of 12 distinct systems, displayed on stage, as well as themselves. The results show trends ranking objects lowest, then robots, then humans, then self, highest. Moreover, objects involved in the show were generally rated higher after the performance. Qualitative responses give further insight into how viewers experienced watching human performers alongside elements of technology. This work lays a framework for measuring human perceptions of robotic systems – and factors that influence this perception – inside live performance and suggests black-that through the lens of expressivity systems of similar type are rated similarly by audience members.

Robot companions are starting to become more common and people are becoming more familiar with devices such as Google Home, Alexa or Pepper, one must wonder what is the optimum way for people to control their devices? This paper presents an investigation into how much direct control people want to have of their robot companion and how dependent this is on the criticality of the tasks the robot performs. A live experiment was conducted in the University of Hertfordshire Robot House, with a robot companion performing four different type of tasks. The four tasks were: booking a doctor’s appointment, helping the user to build a Lego character, doing a dance with the user, and carrying biscuits for the user. The selection of these tasks was based on our previous research to define tasks which were relatively high and low in criticality. The main goal of the study was to find what level of direct control over their robot participants have, and if this was dependent on the criticality of the task performed by the robot. Fifty people took part in the study, and each experienced every task in a random order. Overall, it was found that participants’ perception of control was higher when the robot was performing a task in a semi-autonomous mode. However, for the task “carrying biscuits”, although participants perceived to be more in control with the robot performing the task in a semi-autonomous mode, they actually preferred to have the robot performing the task automatically (where they felt less in control). The results also show that, for the task “booking a doctor’s appointment”, considered to be the most critical of all four tasks, participants did not prefer that the robot chose the date of the appointment as they felt infantilised.

Assistive robots are developed for supporting daily activities of elderly people to uplift the living standards. The assistive robots should be friendly, reliable, active, and comprehensible in order to satisfy the needs of elderly population. Human activities are frequently related to navigational tasks and human tend to use descriptions which include natural language phrases and uncertain terms such as “near”, “little”, “far”, “small”, “large”, “close”to describe about spatial information. Therefore assistive robots should be capable of analysing and understanding descriptions which contain natural language phrases with uncertain terms and creating a conceptual map for effective navigation. This paper proposes a method to understand spatial information in a description with uncertain terms and creates a conceptual map in a robot memory which can be linked with spatial map for purposeful, effective and human friendly navigation task. Human studies have been carried out to study different types of descriptions related to navigation tasks. The Virtual Spatial Data Identifier (VSDI) and Uncertain Term Identifier (UTI) modules have been introduced in order to evaluate the spatial information in description to create a virtual map. Results of the system have been compared with the results of a human study in order to evaluate performance of the proposed system.

When faced with the need of implementing a decentralized behavior for a group of collaborating robots, strategies inspired from swarm intelligence often avoid considering the human operator, granting the swarm with full autonomy. However, field missions require at least to share the output of the swarm to the operator. Unfortunately, little is known about the users’ perception of group behavior and dynamics, and there is no clear optimal interaction modality for swarms. In this paper, we focus on the movement of the swarm to convey information to a user: we believe that the interpretation of artificial states based on groups motion can lead to promising natural interaction modalities. We implement a grammar of decentralized control algorithms to explore their expressivity. We define the expressivity of a movement as a metric to measure how natural, readable, or easily understandable it may appear. We then correlate expressivity with the control parameters for the distributed behavior of the swarm. A first user study confirms the relationship between inter-robot distance, temporal and spatial synchronicity, and the perceived expressivity of the robotic system. We follow up with a small group of users tasked with the design of expressive motion sequences to convey internal states using our grammar of algorithms. We comment on their design choices and we assess the interpretation performance by a larger group of users. We show that some of the internal states were perceived as designed and discuss the parameters influencing the performance.

Embodied art installations embed interactive elements within theatrical contexts and allow participating audience members to experience art in an active, kinesthetic manner. These experiences can exemplify, probe, or question how humans think about objects, each other, and themselves. This paper presents work using installations to explore human perceptions of robot and human capabilities. The paper documents an installation, developed over several months and activated at distinct venues, where user studies were conducted in parallel to a robotic art installation. A set of best practices for successful collection of data over the course of these trials is developed. Results of the studies are presented, giving insight into human opinions of a variety of natural and artificial systems. In particular, after experiencing the art installation, participants were more likely to attribute action of distinct system elements to non-human entities. Post treatment survey responses revealed a direct relationship between predicted difficulty and perceived success. Qualitative responses give insight into viewers’ experiences watching human performers alongside technologies. This work lays a framework for measuring human perceptions of humanoid systems – and factors that influence the perception of whether a natural or artificial agent is controlling a given movement behavior – inside robotic art installations.

We explore the impressions and conceptualisations produced by participants after their first encounter with the teleoperated robot, Telenoid R1. Participants were invited to freely report the first three words that came to mind after seeing the robot. Here we triangulate (i) three-word data from an online survey ( n =340) where respondents saw a brief video of the Telenoid with (ii) three-word data from an interaction study where participants interacted with a physically present Telenoid ( n =75) and, (iii) data from qualitative interviews ( n =7) with participants who had engaged with the Telenoid. Data were subjected to sentiment analysis, linguistic analysis and regression analysis. Ranking of the most frequently produced words in the two groups revealed an overlap on the top-10 produced words (6 out of 10 words). Sentiment analysis and regression revealed an association between negative predicates and the online condition. Sentiments were not convincingly associated with age or gender. Linguistic categorisations of the data revealed that especially adjectives expressing response-dependent features were frequent. We did not find any consistent statistical effect on categorising the words into cognitive and emotional predicates. The proposed three-word method offers, unguided approach to explore initial conceptualisations of robots.

People contacts with social robots, especially humanoid ones, are still rare. The vastness of research shows that we have cognitive relationships. Based on the studies in the field of social psychology, we propose an integrated theoretical model of developing cognitive category “social robot” and the mental model as a result of contact with the robot. We are discussing the consequences of this process, both social and personal, such as cognitive conflicts, unmet expectations and disappointments.

In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained to reconstruct the visual input, while the centre-most hidden representation is also optimized to estimate the state value. Separately, an ensemble of predictive world models generates, based on its learning progress, an intrinsic reward signal which is combined with the extrinsic reward to guide the exploration of the actor-critic learner. Our approach is more data-efficient and inherently more stable than the existing actor-critic methods for continuous control from pixel data. We evaluate our algorithm for the task of learning robotic reaching and grasping skills on a realistic physics simulator and on a humanoid robot. The results show that the control policies learned with our approach can achieve better performance than the compared state-of-the-art and baseline algorithms in both dense-reward and challenging sparse-reward settings.

Cognition and the cognitive processing of sensory information in biological entities is known to occur over multiple layers of processing. In the example of human vision there are a vast number of photo-receptors feeding into various layers of cells which pre-process the original information before it arrives to the brain (as biased data).We propose to use a mechanism known to theoretical biologists as a means to bring about adaptive selforganization in colonies of social insects, and to apply it to such early stage signal processing. The underlying mathematical model is simple, and in the coming years, robotics will move into an era when aggregating simple computation devices into massively large collectives becomes feasible, making it possible to actually build such distributed cognitive sensing systems.

This project designs a smart glove, which can be used for motion tracking in real time to a 3D virtual robotic arm in a PC. The glove is low cost with the price of less than 100 € and uses only internal measurement unit for students to develop their projects on augmented and virtual reality applications. Movement data from the glove is transferred to the PC via UART DMA. The data is set as the motion reference path for the 3D virtual robotic arm to follow. APID feedback controller controls the 3D virtual robot to track exactly the haptic glove movement with zero error in real time. This glove can be used also for remote control, tele-robotics and tele-operation systems.

Visual Perspective Taking (VPT) is the ability to see the world from another person’s perspective, taking into account what they see and how they see it, drawing upon both spatial and social information. Children with autism often find it difficult to understand that other people might have perspectives, viewpoints, beliefs and knowledge that are different from their own, which is a fundamental aspect of VPT. In this research we aimed to develop a methodology to assist children with autism develop their VPT skills using a humanoid robot and present results from our first long-term pilot study. The games we devised were implemented with the Kaspar robot and, to our knowledge, this is the first attempt to improve the VPT skills of children with autism through playing and interacting with a humanoid robot.We describe in detail the standard pre- and post-assessments that we performed with the children in order to measure their progress and also the inclusion criteria derived fromthe results for future studies in this field. Our findings suggest that some children may benefit from this approach of learning about VPT, which shows that this approach merits further investigation.

In this paperwe address the need to design adaptive interacting systems for advanced industrial production machines. Modern production systems have become highly complex and include many subsidiary functionalities, thus making it difficult for least skilled human operators interact with them. In this regard, adapting the behavior of the machine and of the operator interface to the characteristics of the user allows a more effective interaction process, with a positive impact on manufacturing efficiency and user’s satisfaction. To this end, it is crucial to understandwhich are the user’s capabilities that influence the interaction and, hence, should be measured to provide the correct amount of adaptation.Moving along these lines, in this paper we identify groups of users that, despite having different individual capabilities and features, have common needs and response to the interaction with complex production systems. As a consequence,we define clusters of users that have the same need for adaptation. Then, adaptation rules can be defined by considering such users’ clusters, rather than addressing specific individual user’s needs.

Research in social robotics is focused on the development of robots that can provide physical and cognitive support in a socially interactive way. Whilst some studies have previously investigated the importance of user characteristics (age, gender, education, robot familiarity, mood) in the acceptance of social robots as well as the influence a robot’s displayed emotion (positive, negative, neutral) has on the interaction, these two aspects are rarely combined. Therefore, this study attempts to highlight the need to consider the influence that both human and robot attributes can have on social robot acceptance. Eighty-six participants completed implicit and explicit measures of mood before viewing one of three video clips containing a positive, negative or neutral social robot (Pepper) followed by questionnaires on robot acceptance and perception. Gender and education were not associated with acceptance; however, several constructs of the acceptance questionnaire significantly correlated with age and mood. For example, those younger and those experiencing sadness or loneliness were more dependent on the opinions of others (as measured by the social influence construct of the acceptance questionnaire). This highlights the importance of mood in the introduction of social robots into vulnerable populations. Robot familiarity also correlated with robot acceptance with those more familiar finding the robot less useful and less enjoyable, this is important as robots become more prominent in society. Displayed robot emotion significantly influenced acceptance and perception with the positive robot appearing more childlike than the negative and neutral robot, and the neutral robot the least helpful. These findings emphasise the importance of both user and robot characteristics in the successful integration of social robots.

The original article was published in Paladyn, Journal of Behavioral Robotics, 2017, 8(1), 1-17, https://doi.org/10.1515/pjbr-2017-0001. The aim of this erratum is to report the results of a new analysis of the data.