Search

This study examined the affordances of an embodied humanoid robot to engage children in play and learning from the perspective of embodied cognition in two studies as part of multiyear design research. In Study One, we observed how the robot's embodiment, accompanied by its sensors and movements, elicited embodied reactions of eleven children (aged 3 to 6) while they played and learned with the robot one-on-one at home and in school. Two distinct patterns emerged: rich multimodal interaction and fluid learning space. Additionally, the children demonstrated extended attention in the interactions and invited peers into voluntary collaboration. In Study Two, we implemented an interaction triad with ten pairs of children, in which each pair collaborated to help a robot, and observed their collaborative communication while they solved problems involving early academic topics. Three embodied phenomena were noted: (i) embodiment of early mathematics and science knowledge and reasoning, (ii) appropriation of physical space, and (iii) embodied collaboration. Importantly, it was clear in both studies that embodiment occurred not only in thinking but also in social and emotional experiences. We discuss the implications of the findings in relation to the potential of humanoid robots for enabling embodied learning experiences. [ABSTRACT FROM AUTHOR]

This project implements an assistive robotic platform in a mathematics high school class to support the professor's teaching process while analyzing its performance by using a gamification approach and the Octalysis framework. The results obtained from this study evaluated both the platform usability and the general class perception from the students and the professors involved in the experimentation. This paper demonstrates that the implementation of a robotic platform as a supportive tool for the professor improves the class' dynamism and the cooperative behavior of the students, by following the Octalysis approach. Finally, the results helped to determine a better role assignment for future cases where the professor implements a robotic platform during class, leaving the theoretical explanations and class control to the professor, and giving the robot the role of enhancing the activities and review exercises. [ABSTRACT FROM AUTHOR]

The "Computers are social actors" (CASA) assumption (Nass and Moon in J Soc Issues 56:81–103, 2000. https://doi.org/10.1111/0022-4537.00153) states that humans apply social norms and expectations to technical devices. One such norm is to distort one's own response in a socially desirable direction during interviews. However, findings for such an effect are mixed in the literature. Therefore, a new study on the effect of social desirability bias in human–robot evaluation was conducted, aiming for a conceptual replication of previous findings. In a between-subject laboratory experiment, N = 107 participants had to evaluate the robot and the interaction quality after a short conversation in three different groups: In one group, the evaluation was conducted using (1) the same robot of the former interaction, (2) a different robot, (3) a tablet computer. According to the CASA assumption, it was expected, that evaluations on likability and quality of interaction, are higher in the condition with the same robot conducting the evaluation, compared to a different robot or a tablet computer because robots are treated as social actors and hence humans distort ratings in a socially desirable direction. Based on previous findings, we expected robots to evoke higher anthropomorphism and feelings of social presence compared to the tablet computer as potential explanation. However, the data did not support the hypotheses. Low sample size, low statistical power, lack of measurement validation and other problems that could lead to an overestimation of effect sizes—in this study and the literature in general—are discussed in light of the replicability crisis. [ABSTRACT FROM AUTHOR]

In this article, we report on research and creative practice that explores the aesthetic interplay between movement and sound for soft robotics. Our inquiry seeks to interrogate what sound designs might be aesthetically engaging and appropriate for soft robotic movement in a social human-robot interaction setting. We present the design of a soft sound-producing robot, SONŌ, made of pliable and expandable silicone and three sound designs made for this robot. The article comprises an articulation of the underlying design process and results from two empirical interaction experiments (N = 66, N = 60) conducted to evaluate the sound designs. The sound designs did not have statistically significant effects on people’s perception of the social attributes of two different soft robots. Qualitative results, however, indicate that people’s interpretations of the sound designs depend on robot type.

Storytelling plays a central role in human socializing and entertainment, and research on conducting storytelling with robots is gaining interest. However, much of this research assumes that story content is curated. In this paper, we introduce the task of collaborative story generation, where an artificial intelligence agent, or a robot, and a person collaborate to create a unique story by taking turns adding to it. We present a collaborative story generation system which works with a human storyteller to create a story by generating new utterances based on the story so far. Our collaborative story generation system consists of a publicly-available large scale language model that was tuned on a dataset of writing prompts and short stories, and a ranker that samples from the language model and chooses the best possible output. We improve storytelling quality by optimizing the ranker’s sample size to strike a balance between quality and computational cost. Since latency can be detrimental to human-robot interaction, we examine the performance-latency trade-offs of our approach and find the optimal ranker sample size that strikes the best balance between quality and computational cost. We evaluate our system by having human participants play the collaborative story generation game and comparing the stories they create with our system to a naive baseline. Next, we conduct a detailed elicitation survey that sheds light on issues to consider when adapting our collaborative story generation system to a social robot. Finally, in a first step towards allowing human players to control the genre or mood of stories generated, we present preliminary work on steering story generation sentiment polarity with a sentiment analysis model. We find that our proposed method achieves a good balance of steering capability and text coherence. Our evaluation shows that participants have a positive view of collaborative story generation with a social robot and consider rich, emotive capabilities to be key to an enjoyable experience.

At present, industrial robotics focuses more on motion control and vision, whereas humanoid service robotics (HSRs) are increasingly being investigated and researched in the field of speech interaction. The problem and quality of human-robot interaction (HRI) has become a widely debated topic in academia. Especially when HSRs are applied in the hospitality industry, some researchers believe that the current HRI model is not well adapted to the complex social environment. HSRs generally lack the ability to accurately recognize human intentions and understand social scenarios. This study proposes a novel interactive framework suitable for HSRs. The proposed framework is grounded on the novel integration of Trevarthen’s (2001) companionship theory and neural image captioning (NIC) generation algorithm. By integrating image-to-natural interactivity generation and communicating with the environment to better interact with the stakeholder, thereby changing from interaction to a bionic-companionship. Compared to previous research a novel interactive system is developed based on the bionic-companionship framework. The humanoid service robot was integrated with the system to conduct preliminary tests. The results show that the interactive system based on the bionic-companionship framework can help the service humanoid robot to effectively respond to changes in the interactive environment, for example give different responses to the same character in different scenes.

This research presents the technical considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. In this case, this robot responds to the need to achieve technological development in an emerging country with the aim of social impact and social interaction. There are two problems with the social robots currently on the market, which are the main focus of this research. First, their costs are not affordable for companies, universities, or individuals in emerging countries. The second is that their design is exclusively oriented to the functional part with a vision inherent to the engineers who create them without considering the vision, preferences, or requirements of the end users, especially for their social interaction. This last reason ends causing an aversion to the use of this type of robot. In response to the issues raised, a low-cost prototype is proposed, starting from a commercial platform for research development and using open source code. The robot design presented here is centered on the criteria and preferences of the end user, prioritizing acceptability for social interaction. This article details the selection process and hardware capabilities of the robot. Moreover, a programming section is provided to introduce the different software packages used and adapted for the social interaction, the main functions implemented, as well as the new and original part of the proposal. Finally, a list of applications currently developed with the robot and possible applications for future research are discussed.

Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.

Socially assistive robots are being designed to support people’s well-being in contexts such as art therapy where human therapists are scarce, by making art together with people in an appropriate way. A challenge is that various complex and idiosyncratic concepts relating to art, like emotions and creativity, are not yet well understood. Guided by the principles of speculative design, the current article describes the use of a collaborative prototyping approach involving artists and engineers to explore this design space, especially in regard to general and personalized art-making strategies. This led to identifying a goal: to generate representational or abstract art that connects emotionally with people’s art and shows creativity. For this, an approach involving personalized “visual metaphors” was proposed, which balances the degree to which a robot’s art is influenced by interacting persons. The results of a small user study via a survey provided further insight into people’s perceptions: the general design was perceived as intended and appealed; as well, personalization via representational symbols appeared to lead to easier and clearer communication of emotions than via abstract symbols. In closing, the article describes a simplified demo, and discusses future challenges. Thus, the contribution of the current work lies in suggesting how a robot can seek to interact with people in an emotional and creative way through personalized art; thereby, the aim is to stimulate ideation in this promising area and facilitate acceptance of such robots in everyday human environments.

To date, ethical critiques of the use of assistive healthcare robotics have not closely examined the purported care relationship between such robots and their users. Drawing upon the work of care ethics scholars, I argue that authentic care relies upon capacities inherently reciprocal and responsive in nature, which ultimately precludes socially assistive robots from being useful caring tools.

Parent–child story time is an important ritual of contemporary parenting. Recently, robots with artificial intelligence (AI) have become common. Parental acceptance of children’s storytelling robots, however, has received scant attention. To address this, we conducted a qualitative study with 18 parents using the research technique design fiction. Overall, parents held mixed, though generally positive, attitudes toward children’s storytelling robots. In their estimation, these robots would outperform screen-based technologies for children’s story time. However, the robots’ potential to adapt and to express emotion caused some parents to feel ambivalent about the robots, which might hinder their adoption. We found three predictors of parental acceptance of these robots: context of use, perceived agency, and perceived intelligence. Parents’ speculation revealed an uncanny valley of AI: a nonlinear relation between the human likeness of the artificial agent’s mind and affinity for the agent. Finally, we consider the implications of children’s storytelling robots, including how they could enhance equity in children’s access to education, and propose directions for research on their design to benefit family well-being.

Copyright of Interscientific Health Care is the property of University of Thessaly, Medical School / Technological Education Institute, Nursing Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attitudes towards artificial intelligence (AI) and social robots are often depicted as different in Japan, compared to other western countries, such as Sweden. Several different reasons for why there are general differences in attitudes have been suggested. In this study, five hypotheses based on previous literature were investigated. Rather than attempting to establish general differences between groups, subjects were sampled from the respective populations, and correlations between the hypothesized confounding factors and attitudes were investigated within the groups between individuals. The hypotheses in this exploratory study concerned: (H1) animistic beliefs in inanimate objects and phenomena, (H2) worry about unemployment due to AI deployment, (H3) perceived positive or negative portrayal of AI in popular culture, (H4) familiarity with AI, and (H5) relational closeness and privacy with AI. No clear correlations between attitudes and animistic belief (H1), or portrayal of AI in popular culture (H3) could be observed. When it comes to the other attributes, worry about unemployment (H2), familiarity with AI (H4), and relational closeness and privacy (H5), the correlations were similar for the individuals in both groups and in line with the hypotheses. Thus, the general picture following this exploratory study is that individuals in the two populations are more alike than different. [ABSTRACT FROM AUTHOR]

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.

The main aim of the presented study was to check whether the well-established measures concerning the attitude towards humanoid robots are good predictors for the uncanny valley effect. We present a study in which 12 computer rendered humanoid models were presented to our subjects. Their declared comfort level was cross-referenced with the Belief in Human Nature Uniqueness (BHNU) and the Negative Attitudes toward Robots that Display Human Traits (NARHT) scales. Subsequently, there was no evidence of a statistical significance between these scales and the existence of the uncanny valley phenomenon. However, correlations between expected stress level while human-robot interaction and both BHNU, as well as NARHT scales, were found. The study covered also the evaluation of the perceived robots’ characteristic and the emotional response to them.

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 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.

Over decades, social robotics has evolved as a concept that presently covers different areas of application, and interacts with different domains in technology, education, medicine and others. Today, it is possible to envision social robots in tasks that were not expected years ago, and that is not only due to the evolution of social robots, but also to the evolution of the vision humans have for them. This survey addresses recent advances in social robotics from different perspectives. Different contexts and areas of application of social robots are addressed, as well as modalities of interaction with humans. Different robotic platforms used in social contexts are shown and discussed. Relationships of social robotics with advances in other technological areas are surveyed, and methods and metrics used for the human evaluation of the interaction with robots are presented. The future of social robotics is also envisioned based on surveyed works and from different points of view.

Telepresence robots are becoming popular in social interactions involving health care, elderly assistance, guidance, or office meetings. There are two types of human psychological experiences to consider in robot-mediated interactions: (1) telepresence, in which a user develops a sense of being present near the remote interlocutor, and (2) co-presence, in which a user perceives the other person as being present locally with him or her. This work presents a literature review on developments supporting robotic social interactions, contributing to improving the sense of presence and co-presence via robot mediation. This survey aims to define social presence, co-presence, identify autonomous “user-adaptive systems” for social robots, and propose a taxonomy for “co-presence” mechanisms. It presents an overview of social robotics systems, applications areas, and technical methods and provides directions for telepresence and co-presence robot design given the actual and future challenges. Finally, we suggest evaluation guidelines for these systems, having as reference face-to-face interaction.

Artificial models have been used as interactive partners for decades to study the social behavior of animals. Recent technological developments have opened up novel possibilities by allowing researchers to use remote controlled and autonomous objects in these studies, and Animal-Robot Interaction has become an emerging field of behavior science. However, there are theoretical, methodological and practical issues in ethological research using robotic agents. Here we aimed to provide an overview on robots by classifying them along different aspects to facilitate future research, and to present some novel approaches that could be considered as a guide for researchers who are new to the fields of animal-robot interactions (ARI) and human-robot interactions (HRI). The present review may facilitate future collaboration between biologists/psychologists, robot developers, engineers and programmers that further contributes to the development of novel research methods and procedures

Animal-Robot Interaction (ARI) is not a novel approach in the study of animal behavior and cognition, but despite the methodological advances it provides, it is not as widespread as one may expect. The use of interactive, autonomous robots can give benefits which go well beyond the opportunities offered by early stimulus models. Such autonomous robots can help collecting data about animal behavior that is not possible achieve with any other methods. We hope that this collection of reviews and research studies may provide a new push to move this field forward.

The diversified methodology and expertise of interdisciplinary research teams provide the opportunity to overcome the limited perspectives of individual disciplines. This is particularly true at the interface of Robotics, Neuroscience, and Psychology as the three fields have quite different perspectives and approaches to offer. Nonetheless, aligning backgrounds and interdisciplinary expectations can present challenges due to varied research cultures and practices. Overcoming these challenges stands at the beginning of each productive collaboration and thus is a mandatory step in cognitive neurorobotics. In this article, we share eight lessons that we learned from our ongoing interdisciplinary project on human-robot and robot-robot interaction in social settings. These lessons provide practical advice for scientists initiating interdisciplinary research endeavors. Our advice can help to avoid early problems and deal with differences between research fields, prepare for and anticipate challenges, align project expectations, and speed up research progress, thus promoting effective interdisciplinary research across Robotics, Neuroscience, and Psychology.

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.

Researchers continue to devise creative ways to explore the extent to which people perceive robots as social agents, as opposed to objects. One such approach involves asking participants to inflict ‘harm’ on a robot. Researchers are interested in the length of time between the experimenter issuing the instruction and the participant complying, and propose that relatively long periods of hesitation might reflect empathy for the robot, and perhaps even attribution of human-like qualities, such as agency and sentience. In a recent experiment, we adapted the so-called ‘hesitance to hit’ paradigm, in which participants were instructed to hit a humanoid robot on the head with a mallet. After standing up to do so (signaling intent to hit the robot), participants were stopped, and then took part in a semi-structured interview to probe their thoughts and feelings during the period of hesitation. Thematic analysis of the responses indicate that hesitation not only reflects perceived socialness, but also other factors including (but not limited to) concerns about cost, mallet disbelief, processing of the task instruction, and the influence of authority. The open-ended, free responses participants provided also offer rich insights into individual differences with regards to anthropomorphism, perceived power imbalances, and feelings of connection toward the robot. In addition to aiding understanding of this measurement technique and related topics regarding socialness attribution to robots, we argue that greater use of open questions can lead to exciting new research questions and interdisciplinary collaborations in the domain of social robotics.

Verbal communication is an expanding field in robotics showing a significant increase in both the industrial and research field. The application of verbal communication in robotics aims to reach a natural human-like interaction with robots. In this study, we investigated how salient terms related to verbal communication in robotics have evolved over the years, what are the topics that recur in the related literature, and what are their trends. The study is based on a computational linguistic analysis conducted on a database of 7,435 scientific publications over the last 2 decades. This comprehensive dataset was extracted from the Scopus database using specific key-words. Our results show how relevant terms of verbal communication evolved, which are the main coherent topics and how they have changed over the years. We highlighted positive and negative trends for the most coherent topics and the distribution over the years for the most significant ones. In particular, verbal communication resulted in being highly relevant for social robotics. Potentially, achieving natural verbal communication with a robot can have a great impact on the scientific, societal, and economic role of robotics in the future.