While research in human–robot interaction is beginning to focus on the acceptance of domestic robots, there is little research on the potential adoption of these agents. Technology adoption is a complex phenomenon requiring not only positive perceptions of technology but also its value, along with product desire strong enough to lead to desired adoption behavior. Adoption of innovations also occurs in phases, from early adopters to mainstream consumers, then laggards. While the characteristics of technology early adopters generally have been researched extensively, there is no previous work that seeks to validate some of these variables for domestic robots specifically, and which draws from HRI research to further amend them. In this work, we determine how various consumer and robot characteristics affect assessments of home robot liking, privacy concerns, and purchasing intent. We find that five main consumer characteristics are associated with robot early adopters, and that surprisingly, income has a negative correlation with purchasing intent, specifically for the companion robot. We further compare predicted product liking to purchasing intent, showing that although robot acceptance is reasonably high for those in the mainstream, purchasing intent is low. For all market segments, perceptions of high price accounted for about 20–30% of the variance in intended purchasing, intended use 6–7%, belief in performance as advertised 4–6%, and design 3%. While privacy concerns were not influential to purchasing intentions held by early adopters, they were to mainstream users and laggards.

Evaluation of robots commonly occurs using various stimuli, including photos, videos, and live interaction. However, a better understanding of how and why chosen stimuli affect perceptions, and how evaluations using lower fidelity media (e.g. photos) compare to evaluations using higher context stimuli (e.g., videos), is needed. Through a survey of 599 M-Turk participants, we compare robot evaluations based on exposure to three types of media - photos, GIFs, and promotional videos. We analyze nine perception and behavioral intention measures of three home robots with varying levels of anthropomorphism (Olly, Jibo, and Liku): overall liking, liking of appearance, liking of intended use, eeriness, human-likeness, performance expectations, privacy concerns, information seeking intention, and purchase intention. We find that photos consistently differ from ratings of videos for all measures, except for liking of robots' intended use. Use of GIFs led to measurements in line with videos for seven of the nine measurements, due to the importance of movement in perceptual assessments and character judgments (e.g., friendly, creepy). Except for the most human-like robot, neither photos nor GIFs captured human-likeness to a similar degree as videos, due to the importance of speech in assessments. Though GIFs captured informational and overall privacy concerns well, they did not adequately capture physical privacy concerns.

Robots may be able to significantly assist older adults through making activity recommendations. Prior research suggests that gender and age of a robot’s voice may affect how people respond to such recommendations, but few studies have explored how a robot’s voice is perceived by older adults, and whether their perceptions differ across cultures. We conducted a survey study with older adult participants (aged 65+) in the U.S. (N=225) and Japan (N=466), asking them to evaluate a humanoid robot speaking with three different voices (male, female, child). After seeing a video of a robot making recommendations, participants rated the fit of the voice to the robot, its sociality (via the Robotic Social Attributes Scale - RoSAS), and their willingness to use the robot in various contexts. We discovered that robot’s social attributes and participants’ culture impacted willingness to use the robot in both countries. Having positive social attributes and lower negative attributes increases willingness to use the robot. The U.S. older adults preferred the adult robot voices, had more positive social attributes, less negative social attributes, and were more likely to accept lifestyle recommendations than Japanese older adults. This study contributes to our understanding of older adults’ perceptions of robot voice and provides design implications for robots that make recommendations to older adults.

In this paper, we present an in-depth illustration of interaction failures relatively unexplored in the field of human-robot interaction (HRI). Our qualitative analysis of interactions between a social robot and 12 participants sheds light on different types of erroneous interactions initiated by human and robot actors and their outcomes. Our findings show that a small portion of observed failures had fatal impacts on interactions. In most cases, they had little negative effects on interactions or even led to favorable outcomes, causing laughter and giggling from participants, for example. Overall, our study calls for further examination of the roles of failures and contextual factors that influence the consequences of failures in HRI.

Previous work suggests that older adults’ meaning and happiness may be increased simply by having them engage in self-reflection exercises. Therefore, we design four modules to promote self-reflection, delivered by the QT robot. These modules were created with reference to three different time orientations — past, present, and future — and by incorporating aspects of life satisfaction and the PERMA model of well-being. Results show that about half of our older adult participants experienced subjective changes in meaning, happiness, and desire to make positive life changes during each module, with 11 of 15 participants experiencing changes to one of these measures after engaging in all four interactions. We make several suggestions for updating these modules for autonomous and longer-term deployment using the robot.

Robot-assisted language learning (RALL) is becoming a more commonly studied area of human-robot interaction (HRI). This research draws on theories and methods from many different fields, with researchers utilizing different instructional methods, robots, and populations to evaluate the effectiveness of RALL. This survey details the characteristics of robots used—form, voice, immediacy, non-verbal cues, and personalization—along with study implementations, discussing research findings. It also analyzes robot effectiveness. While research clearly shows that robots can support native and foreign language acquisition, it has been unclear what benefits robots provide over computer-assisted language learning. This survey examines the results of relevant studies from 2004 (RALL's inception) to 2017. Results suggest that robots may be uniquely suited to aid in language production, with apparent benefits in comparison to other technology. As well, research consistently indicates that robots provide unique advantages in increasing learning motivation and in-task engagement, and decreasing anxiety, though long-term benefits are uncertain. Throughout this survey, future areas of exploration are suggested, with the hope that answers to these questions will allow for more robust design and implementation guidelines in RALL.