International Journal of Social Robotics最新文献

Abstract

Neurodevelopmental disorders (NDD) are a group of conditions affecting children’s neurodevelopment with consequences on personal, social, and educational functioning. Social robots have been used in the rehabilitation of children with NDD with encouraging results on learning outcomes. This study aims at understanding how a social robot should act to support caregivers during the rehabilitation of children with NDD. Through a Design-Based-Research approach, we investigate this question by considering the point of view of the most concerned and expert people, i.e., children with NDD and their caregivers. We present here the collaborative and iterative design of R2C3, a social robot used to support caregivers and children during rehabilitation sessions in a learning-by-teaching scenario. 27 caregivers and 6 children participated in the iterative design and/or the evaluation of R2C3, that resulted in the development of a Wizard-of-Oz interface and a library containing 120 robot behaviors. We then studied how caregivers used such behaviors during the rehabilitation sessions. We found they mainly used the robot to provide positive reinforcements to children, to elicit their reflection and knowledge toward shared handwriting activities, and to support children’s error acceptance. However, the utilization of Positive Reinforcement by caregivers tends to decrease significantly as the sessions progress.

Increasingly, Information and Communication Technologies (ICT) are being used as a resource in the teaching–learning process of students with special educational needs. Specifically, the needs of children with autism spectrum disorder (ASD) seem to align perfectly with ICT, especially with Pedagogical Robotics, as it enables the creation of predictable and controllable environments. For this reason, the main aim of this research has been to apply the Bee-Bot robot to improve the social-emotional reciprocity responses of students with ASD. Therefore, a quantitative research based on a quasi-experimental methodology and a pre-test-post-test design has been developed. The sample of participants was composed by 22 children with ASD. In this sense, the experimental group was composed by 11 students who developed the activities through the mediation of the Bee-Bot robot. While the control group was composed by 11 other students who developed non-robotics mediated activities. An ad hoc instrument consisting of 44 items divided into three dimensions, related to the communication and social interaction area, was used. Each participant participated in ten individual sessions lasting fifteen minutes. The results indicate improvements in the post-test because of the use of robotics as a mediating learning tool. For example, there are improvements in items 1 and 2 related to attention with p values less than 0.05, as well as improvements in items 13, 15 and 16 related to the identification and discrimination of emotions. Furthermore, the results of the intra-group analysis show, on the one hand, significant differences on the part of the control group in item 13, related to emotion identification and discrimination skills. On the other hand, in the case of the experimental group, the number of items in which there are significant differences is greater. Specifically, participants in the experimental group show significant differences in items related to attention, basic social skills, conversational skills and emotion identification skills. In conclusion, the Bee-Bot robot, accompanied by a pedagogical programme, has potential for learning social-emotional reciprocity skills in these students.

Artificial intelligence (AI) and robots have the potential to revolutionize society, with impacts ranging from the broadest reaches of industry and policy to the minutiae of daily life. The extent to which AI-based technologies can bring benefits to human society depends on how people perceive them––folk beliefs of AI and robots. The present paper aims to gain insights into people’s perspectives on artificial intelligence and robots by examining their folk beliefs. In Study 1, we explored folk beliefs regarding general artificial intelligence and robots using metaphor nomination (Phase 1, N = 99), factor analysis (Phase 2, N = 267), and semantic analysis (Phase 3). Results indicated three primary folk beliefs for AI: the unknown, the assistants, and the machines. For robots, three primary folk beliefs emerged: the assistants, the companions, and the tools. In Study 2, we investigated folk beliefs about robots in various application contexts through free listing (Phase 1, N = 82) and factor analysis (Phase 2, N = 300). Results revealed four folk beliefs for companion robots: companion ability, applicable target, social consequence, and technology. Additionally, four folk beliefs emerged for education robots: educational ability, advantage, disadvantage, and technology, while medical robots were associated with five folk beliefs: medical ability, advancement, social consequence, disadvantage, and technology. This research is the first step in examining how ordinary people conceptualize artificial intelligence and robots through folk theories, unveiling several directions for future research reference. Our findings also revealed that lay people’s perceptions of artificial intelligence and robots are shaped by social cognitive processes. This also implies that the methods of folk theories can be utilized to investigate people’s social cognitive processes. The current study carries practical significance for the designers and manufacturers of AI and robots, guiding aspects such as the professional capabilities of artificial intelligence and robots, potential negative social consequences, and the needs of specific user groups.

Robot touch is a vital interaction mode for emotional communication and human mental support in HRI. However, little is known about how robot touch characteristics influence the users’ subjective perception of emotion and physiological reactions. Therefore, a within-subject experiment of robot touches was conducted with the touch type (contact versus grip), length (long versus short), and location (hand versus forearm) as independent variables. The subjective perception was measured with PANAS scales. Electrocardiography (ECG) and functional near-infrared spectroscopy (fNIRS) were utilized to measure the participant’s cardiac autonomic nervous system responses and cerebral central nervous system responses. Results showed that touch type and length jointly affected users’ subjective perception of emotion and cerebral activity, and location affected users’ heart rate variability and cerebral activity. The results suggest that robot short-grip and long-contact behaviors might bring users more positive emotions. Although robot touch on forearms might not induce more positive emotional perception, it might be helpful for emotional regulation and might mobilize more cognitive resources.

The ANA Avatar XPRIZE was a four-year competition to develop a robotic “avatar” system to allow a human operator to sense, communicate, and act in a remote environment as though physically present. The competition featured a unique requirement that judges would operate the avatars after less than one hour of training on the human–machine interfaces, and avatar systems were judged on both objective and subjective scoring metrics. This paper presents a unified summary and analysis of the competition from technical, judging, and organizational perspectives. We study the use of telerobotics technologies and innovations pursued by the competing teams in their avatar systems, and correlate the use of these technologies with judges’ task performance and subjective survey ratings. It also summarizes perspectives from team leads, judges, and organizers about the competition’s execution and impact to inform the future development of telerobotics and telepresence.

This paper describes the cybernetic avatar system developed by Team JANUS for connectivity, exploration, and skill transfer: the core domains targeted by the ANA Avatar XPRIZE competition, for which Team JANUS was a finalist. We used as an avatar a humanoid robot with a human-like appearance and shape that is capable of reproducing facial expressions and walking, and built an avatar control system that allowed the operator to control the avatar through equivalent mechanisms of motion; that is, by replicating the upper-body movement with naturalness and by stepping to command locomotion. In this way, we aimed to achieve high-fidelity telepresence and managed to be well evaluated from the point of view of the operator during the competition. We introduce our solutions to the integration challenges and present experimental results to asses our avatar system, together with current limitations and how we are planning to mitigate them in future work.

In real-life teleoperation scenarios, the presence of time-varying network delays, particularly in wireless networks, poses significant challenges in maintaining stability in a bilateral teleoperation system. Various approaches have been proposed in the past to address stability concerns; however, these often come at the expense of system transparency. Nevertheless, increasing transparency is crucial in a teleoperation system to enable precise and safe operations, as well as to provide real-time decision-making capabilities for the operator. This paper presents our comprehensive approaches to maximize teleoperation transparency by minimizing system impedance, enhance the wave variable method to handle time-varying network delays, and alleviate non-smooth effects caused by network jitters in bilateral teleoperation. The proposed methodologies take into account the real-world challenges and considerations to ensure the practical applicability and effectiveness of the teleoperation system. Throughout these implementations, passivity analysis is employed to ensure system stability, guaranteeing a reliable and safe teleoperation experience. The proposed approaches were successfully validated in Team Northeastern’s Avatar telepresence system, which achieved the 3rd place in ANA Avatar XPRIZE challenge.

The relevance of trust on the road to successful human-robot interaction is widely acknowledged. Thereby, trust is commonly understood as a monolithic concept characterising dyadic relations between a human and a robot. However, this conceptualisation seems oversimplified and neglects the specific interaction context. In a multidisciplinary approach, this conceptual analysis synthesizes sociological notions of trust and distrust, psychological trust models, and ideas of philosophers of technology in order to pave the way for a multidimensional, relational and context-sensitive conceptualisation of human-robot trust and distrust. In this vein, trust is characterised functionally as a mechanism to cope with environmental complexity when dealing with ambiguously perceived hybrid robots such as collaborative robots, which enable human-robot interactions without physical separation in the workplace context. Common definitions of trust in the HRI context emphasise that trust is based on concrete expectations regarding individual goals. Therefore, I propose a three-dimensional notion of trust that binds trust to a reference object and accounts for various coexisting goals at the workplace. Furthermore, the assumption that robots represent trustees in a narrower sense is challenged by unfolding influential relational networks of trust within the organisational context. In terms of practical implications, trust is distinguished from acceptance and actual technology usage, which may be promoted by trust, but are strongly influenced by contextual moderating factors. In addition, theoretical arguments for considering distrust not only as the opposite of trust, but as an alternative and coexisting complexity reduction mechanism are outlined. Finally, the article presents key conclusions and future research avenues.

Social learning is an evolutionarily important ability increasingly attributed also to invertebrate species. Interfacing robots with animals represents a promising strategy to investigate social learning. Herein, we studied if the gregarious form of Locusta migratoria, a particularly suited model to examine social learning, can use social information provided by robotic demonstrators to optimize their predator avoidance. Robotic demonstrators with different silhouettes and colours (biomimetic or neutral) were used to investigate if their rotation on a rod (e.g. hiding behaviour) elicited the same behaviour in neighbouring locusts. Locusts’ responses were affected by different robotic demonstrators, observing a significant impact of the biomimetic silhouette in reducing the latency duration, and in promoting social learning (e.g. locusts displaying hiding behaviour after observing it in robotic demonstrators). A significant impact of colour patterns in triggering socially induced hiding behaviour was also recorded, especially when the biomimetic silhouette was coloured with the gregarious-like pattern. This research indicates gregarious locusts exploit social information in specific ecological contexts, providing basic knowledge on the complex behavioural ecology and social biology in invertebrates. The proposed animal-robot interaction paradigm shows the role of robots as carrier of social information to living organisms, suggesting social biorobotics as advanced and sustainable approach for socio-biology investigation, and environmental management.

As robotic agents become increasingly pervasive in our lives, recent works have shown promising results on the use of emotions on social drones. However, further research is needed to determine the appropriateness of these emotions in various contexts. We here investigate the appropriateness of seven drone emotional states. In a series of six workshops ((N{=}30)) consisting of both individual and group activities, we studied emotion appropriateness for six drone metaphors across the two most popular and radically different domains for Human–Drone Interaction namely: Emergency and Entertainment. Within diverse situations of interactions, participants were able to identify the appropriateness of each emotion. Our results describe how each emotion was found both appropriate and inappropriate depending on context. We provide insights into unique opportunities generated by the perceived emotion appropriateness, depending on different roles of drone emotions in interactions with people. We conclude with design considerations for future social robotic agents, including the importance of using a broad range of emotions, the use of a neutral expression, the temporality of emotions, and novel applications to interaction design. This work contributes to the understanding of the inner workings of emotion appropriateness in drones, providing researchers with a starting point for future work on social flying robots.