International Journal of Social Robotics最新文献

While interactions with social robots are novel and exciting for many people, one concern is the extent to which people’s behavioural and emotional engagement might be sustained across time, since during initial interactions with a robot, its novelty is especially salient. This challenge is particularly noteworthy when considering interactions designed to support people’s well-being, with limited evidence (or empirical exploration) of social robots’ capacity to support people’s emotional health over time. Accordingly, our aim here was to examine how long-term repeated interactions with a social robot affect people’s self-disclosure behaviour toward the robot, their perceptions of the robot, and how such sustained interactions influence factors related to well-being. We conducted a mediated long-term online experiment with participants conversing with the social robot Pepper 10 times over 5 weeks. We found that people self-disclose increasingly more to a social robot over time, and report the robot to be more social and competent over time. Participants’ moods also improved after talking to the robot, and across sessions, they found the robot’s responses increasingly comforting as well as reported feeling less lonely. Finally, our results emphasize that when the discussion frame was supposedly more emotional (in this case, framing questions in the context of the COVID-19 pandemic), participants reported feeling lonelier and more stressed. These results set the stage for situating social robots as conversational partners and provide crucial evidence for their potential inclusion in interventions supporting people’s emotional health through encouraging self-disclosure.

The field of human–drone interaction (HDI) has investigated an increasing number of applications for social drones, all while focusing on the drone’s inherent ability to fly, thus overpassing interaction opportunities, such as a drone in its perched (i.e., non-flying) state. A drone cannot constantly fly and a need for more realistic HDI is needed, therefore, in this exploratory work, we have decoupled a social drone’s flying state from its perched state and investigated user interpretations of its physical rendering. To do so, we designed and developed BiRDe: a Bodily expressIons and Respiration Drone conveying Emotions. BiRDe was designed to render a range of emotional states by modulating its respiratory rate (RR) and changing its body posture using reconfigurable wings and head positions. Following its design, a validation study was conducted. In a laboratory study, participants (({N}={30})) observed and labeled twelve of BiRDe’s emotional behaviors using Valence and Arousal based emotional states. We identified consistent patterns in how BiRDe’s RR, wings, and head had influenced perception in terms of valence, arousal, and willingness to interact. Furthermore, participants interpreted 11 out of the 12 behaviors in line with our initial design intentions. This work demonstrates a drone’s ability to communicate emotions even while perched and offers design implications and future applications.

The willingness to use service robots plays a pivotal role in human–robot interaction. To establish a valid measure in the Chinese context, this study aimed to revisit the validity and reliability of the Service Robot Integration Willingness (SRIW) Scale among Chinese adults. A total of 955 participants were recruited to complete the Chinese version of the SRIW. Our findings revealed a four-factor model comprising 31 items, indicating a strong model fit. Furthermore, trust in automation correlated positively with the Chinese SRIW, while negative attitudes toward robots exhibited a significant inverse correlation, supportting the Chinese SRIW’s substantial criterion-related validity. In conclusion, this article introduces an updated Chinese SRIW, underscoring its efficacy in measuring the readiness to adopt service robots in China.

The discussion around gendering humanoid robots has gained more traction in the last few years. To lay the basis for a full comprehension of how robots’ “gender” has been understood within the Human–Robot Interaction (HRI) community—i.e., how it has been manipulated, in which contexts, and which effects it has yielded on people’s perceptions and interactions with robots—we performed a scoping review of the literature. We identified 553 papers relevant for our review retrieved from 5 different databases. The final sample of reviewed papers included 35 papers written between 2005 and 2021, which involved a total of 3902 participants. In this article, we thoroughly summarize these papers by reporting information about their objectives and assumptions on gender (i.e., definitions and reasons to manipulate gender), their manipulation of robots’ “gender” (i.e., gender cues and manipulation checks), their experimental designs (e.g., demographics of participants, employed robots), and their results (i.e., main and interaction effects). The review reveals that robots’ “gender” does not affect crucial constructs for the HRI, such as likability and acceptance, but rather bears its strongest effect on stereotyping. We leverage our different epistemological backgrounds in Social Robotics and Gender Studies to provide a comprehensive interdisciplinary perspective on the results of the review and suggest ways to move forward in the field of HRI.

Modern developments in speech-enabled drones and aerial manipulation systems (AMS) enable drones to have social interactions with people, which is important for therapeutic applications involving flight and above-eye-level monitoring in people’s homes, but not everyone will accept drones into their daily lives. Consistently assessing who would accept a socially assistive drone into their home is a challenge for roboticists. An animation-based Mechanical Turk survey (N = 176) found that acceptance of a voice-enabled AMS for fatigue – i.e., physical or mental tiredness in the participant’s life – was higher among younger adults with higher education and longer symptoms of fatigue, suggesting demographics and a need for the task performed by the drone are critical factors for drone acceptance. Participants rated the drone as more acceptable for others than for themselves, demonstrating a self-other effect. A second video-based YouGov survey (N = 404) found that younger adults rated an AMS for managing the symptom of day-to-day fatigue as more acceptable than older adults. The self-other effect was reduced among participants who read a situation with specific versus general phrasing of the AMS’s imagined use, suggesting that it may be caused by an attribution bias. These results demonstrate how analyzing demographics and specifying the wording of technology use can more consistently assess to whom drones for fatigue are acceptable, which is of interest to public opinion researchers and roboticists.