International Journal of Social Robotics最新文献

Our world is being increasingly pervaded by intelligent robots with varying degrees of autonomy. To seamlessly integrate themselves in our society, these machines should possess the ability to navigate the complexities of our daily routines even in the absence of a human’s direct input. In other words, we want these robots to understand the intentions of their partners with the purpose of predicting the best way to help them. In this paper, we present the initial iteration of cognitive architecture for social perception and engagement in robots: a symbolic cognitive architecture that uses qualitative spatial reasoning to anticipate the pursued goal of another agent and to calculate the best collaborative behavior. This is performed through an ensemble of parallel processes that model a low-level action recognition and a high-level goal understanding, both of which are formally verified. We have tested this architecture in a simulated kitchen environment and the results we have collected show that the robot is able to both recognize an ongoing goal and to properly collaborate towards its achievement. This demonstrates a new use of qualitative spatial relations applied to the problem of intention reading in the domain of human–robot interaction.

Developing an authentic understanding of potential users’ needs and translating these into usable categories as an input to research and development is an open problem. It is generally accepted that genuine knowledge of user needs is essential for the creation of any new technology. For assistive robots, however, this knowledge is even more important for two key reasons. First, because the form and function of these technologies is still in the process of negotiation, and second, because assistive robots are ultimately intended for a vulnerable population. In this paper, we describe a number of existing strategies to address this challenge and discuss some of their shortcomings, including a loss of data richness and context, the stereotyping of users and a lack of transparency and traceability. The primary contribution of this paper is a novel Authentic Citations process for capturing needs which aims to address these shortcomings. This process involves a thematic analysis of complex qualitative data to derive robotics needs for older people, which emphasises the retention of the original situated description, or ‘authentic citation’, for ongoing sensitising and grounding at all stages of the research and development cycle, and by various stakeholders. The Authentic Citations process adds additional rigour to a process that can be tacit and opaque and can be used by robotics researchers to analyse and translate qualitative research into usable categories. An additional contribution of this paper is an initial outline of a taxonomy of assistive robotics needs for older people, which contributes to improving the understanding of the user as a situated and complex person and can be used as an input to design.

Abstract

Numerous studies have investigated proxemics in the context of human–robot interactions, but little is known about whether these insights can be applied to human–drone interactions (HDI). As drones become more common in social settings, it is crucial to ensure they navigate in a socially acceptable and human-friendly way. Understanding how individuals position themselves around drones is vital to promote user well-being and drones’ social acceptance. However, real-world constraints and risks associated with drones flying in close proximity to participants have limited research in this field. Virtual reality is a promising alternative for investigating HDI, as prior research suggests. This paper presents a proxemic user study (N = 45) in virtual reality, examining how drone height and framing influence participants’ proxemic preferences. The study also explores participants’ perceptions of social drones and their vision for the future of flying robots. Our findings show that drone height significantly impacts participants’ preferred interpersonal distance, while framing had no significant effect. Thoughts on how participants envision social drones (e.g., interaction, design, applications) reveal interpersonal differences but also shows overall consistency over time. While the study demonstrates the value of using virtual reality for HDI experiments, further research is necessary to determine the generalizability of our findings to real-world HDI scenarios.

Although most studies that seek to measure participants’ judgments and attitudes regarding humanoid robots’ possessing (or appearing to possess) a mind or mental capacities have been based on verbal measures, there is yet no standard psychometric instrument for this end. Using a COSMIN approach, this critical review seeks to summarize the most valid and reliable self-report instruments that aim to measure mental state attribution to humanoid robots. 501 papers were reviewed, but only 11 were included, finding that: (1) The instruments do not usually measure mental state attribution toward robots as an exclusive phenomenon but as a factor associated with the tendency to anthropomorphize non-human entities; (2) There is a lack of consensus regarding a definition of mental state attribution and the psychometric dimensions that underlie it; (3) The tendency to anthropomorphize does not by itself imply the attribution of mind to robots. In our discussion, we delve into the general problem of mind perception/attribution and speculate on the possible theoretical basis for a multifactorial model for measuring mind perception as part of a broader phenomenon we term “psycheidolia.”

Abstract

The design of robots for everyday use should take into account the specific nature of the individual end-user and the possibility of interactions with multiple users in diverse scenarios, promoting versatility and increasing the chances of their successful adoption in everyday environments. Most robots are designed, however, to perform tasks and interact in typical social scenarios with an abstract human user. We observed a recent surge in the use of accessories with social robots, which aligns with a broader trend of consumers’ preference for personalizing the technologies they interact with. Drawing from the concepts of adaptability and customizability in collaborative systems, we explore the potential use of accessory-like items for social robots to enable low-tech customization and user appropriation, thus enhancing their value and suitability in various social situations. We draw from Human-Computer Interaction and Computer-Supported Co-operative Work literature to show how end-user customizability and appropriation are essential, but less frequently considered, in the study and design of social robots. We conceptualize Social Robot Accessories (SRAs) as a way for end-users to customize robots, and present three studies - (1) a literature survey on accessory-like item use with social robots, (2) a survey of commercially available robot accessories, and (3) a Twitter-based analysis of accessory use for AIBO and LOVOT robots by their users. We use findings from these studies to envision a design space of SRAs for use by Human-Robot Interaction (HRI) researchers.

The human–robot interaction (HRI) field goes beyond the mere technical aspects of developing robots, often investigating how humans perceive robots. Human perceptions and behavior are determined, in part, by expectations. Given the impact of expectations on behavior, it is important to understand what expectations individuals bring into HRI settings and how those expectations may affect their interactions with the robot over time. For many people, social robots are not a common part of their experiences, thus any expectations they have of social robots are likely shaped by other sources. As a result, individual expectations coming into HRI settings may be highly variable. Although there has been some recent interest in expectations within the field, there is an overall lack of empirical investigation into its impacts on HRI, especially in-person robot interactions. To this end, a within-subject in-person study ((N=31)) was performed where participants were instructed to engage in open conversation with the social robot Pepper during two 2.5 min sessions. The robot was equipped with a custom dialogue system based on the GPT-3 large language model, allowing autonomous responses to verbal input. Participants’ affective changes towards the robot were assessed using three questionnaires, NARS, RAS, commonly used in HRI studies, and Closeness, based on the IOS scale. In addition to the three standard questionnaires, a custom question was administered to capture participants’ views on robot capabilities. All measures were collected three times, before the interaction with the robot, after the first interaction with the robot, and after the second interaction with the robot. Results revealed that participants to large degrees stayed with the expectations they had coming into the study, and in contrast to our hypothesis, none of the measured scales moved towards a common mean. Moreover, previous experience with robots was revealed to be a major factor of how participants experienced the robot in the study. These results could be interpreted as implying that expectations of robots are to large degrees decided before interactions with the robot, and that these expectations do not necessarily change as a result of the interaction. Results reveal a strong connection to how expectations are studied in social psychology and human-human interaction, underpinning its relevance for HRI research.

This conceptual paper presents a novel framework for the design and study of social robots that support well-being. Building upon the self-determination theory and the associated Motivation, Engagement, and Thriving in User Experience (METUX) model, this paper argues that users’ psychological basic needs for autonomy, competence, and relatedness should be put at the center of social robot design. These basic needs are essential to people’s psychological well-being, engagement, and self-motivation. However, current literature offers limited insights into how human–robot interactions are related to users’ experiences of the satisfaction of their basic psychological needs and thus, to their well-being and flourishing. We propose that a need-fulfillment perspective could be an inspiring lens for the design of social robots, including socially assistive robots. We conceptualize various ways in which a psychological need-fulfillment perspective may be incorporated into future human–robot interaction research and design, ranging from the interface level to the specific tasks performed by a robot or the user’s behavior supported by the robot. The paper discusses the implications of the framework for designing social robots that promote well-being, as well as the implications for future research.

Humanoid store staff is increasingly common. Many retailers rely on automation to address the mounting pressure for cost savings. Meanwhile, humanoid store staff can influence customer experience negatively. People feel stressed and eerie about interacting with humanoids. In this article, I investigate one way to improve people’s attitudes toward humanoids. People’s negative attitudes towards humanoids, such as fear or a sense of eeriness, can be substantially mitigated when they are in a crowded environment. People in a crowded environment do not fear robots or find robots uncanny while people in a relatively uncrowded environment do. I refer to this phenomenon as the “crowd effect.” Being in a crowded environment substantially changes how people evaluate and respond to humanoids. The effect is mediated by risk aversion tendency.

As dementia-induced impairments of daily functioning escalate, novel cognitive stimulation techniques utilizing technological advances, like social robots, have surfaced. This study examines the interaction level of the EBO social-care robot with day center patients in Cáceres, Extremadura, Spain. The study uses systematic video analysis as a method of interaction assessment. This observational pilot study was performed on patients above 65 with mild to moderate cognitive impairment (Minimental State Examination (ge ) 21) receiving cognitive therapy at the AZTIDE social and health center. Two individualized 10–15 min sessions, replicating the Wizard of Oz technique, were conducted per participant, with the human operator’s commands being unnoticeably executed by the EBO robot. Of the six participants involved, all maintained complete eye contact with the robot, with 83.3(%) of the interactions recording maximum attention. Participants felt comfortable and calm, rating conversational factors such as attentiveness and naturalness as ’good’ or ‘excellent’. The high interaction level with the EBO robot suggests it as a promising tool for cognitive stimulation in patients with mild to moderate cognitive impairment. The systematic video evaluation also appears effective in assessing user–robot interaction, thus underscoring its potential utility in future social robotics research.

Personal assistance robots are making inroads into our private and public life. At the same time, most humans are still unfamiliar with this technology and hesitate to accept and use it for daily tasks. Fortunately, the designs of robots can be adjusted to yield greater acceptance, subsequently enabling their utilization across various tasks. Using a scenario-based online experiment, we explored how sociability (low vs. high), ownership (private vs. public), and affinity for technology influence the acceptance and intention to use a robot for grocery shopping. Moreover, to assess users’ preference for robots’ morphology, participants were asked to choose a robot (technical vs. anthropomorphic design) that they would prefer to use in a supermarket. We found that low sociability of the service robot and a higher affective affinity for technology led to a higher level of acceptance. For more sociable robots, higher levels of anthropomorphism were preferred. Our results point to the importance of task-specific robot design that exceeds functional considerations.