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There is a knowledge gap regarding which types of failures robots undergo in domestic settings and how these failures influence customer experience. We classified 10,072 customer reviews of small utilitarian domestic robots on Amazon by the robotic failures described in them, grouping failures into twelve types and three categories (Technical, Interaction, and Service). We identified sources and types of failures previously overlooked in the literature, combining them into an updated failure taxonomy. We analyzed their frequencies and relations to customer star ratings. Results indicate that for utilitarian domestic robots, Technical failures were more detrimental to customer experience than Interaction or Service failures. Issues with Task Completion and Robustness and Resilience were commonly reported and had the most significant negative impact. Future failure-prevention and response strategies for utilitarian domestic robots should address the robot's technical ability to meet functional goals, operate and maintain structural integrity over time. Usability and interaction design were less detrimental to customer experience, indicating that customers may be more forgiving of failures that impact these aspects for the robots and practical uses examined. Further, we developed a Natural Language Processing model capable of predicting whether a customer review contains content that describes a failure and the type of failure it describes. With this knowledge, designers and researchers of robotic systems can prioritize design and development efforts towards essential issues. [ABSTRACT FROM AUTHOR]

Previous research in human-robot interaction has explored using robots to increase objective and hedonic aspects of well-being and quality of life, but there is no literature on how robots might be used to support eudaimonic aspects of well-being (such as meaning in life). A sense of meaning has been shown to positively affect health and longevity. We frame our study around the Japanese concept of ikigai, which is widely used with Japanese older adults to enhance their everyday lives, and is closely related to the concept of eudaimonic well-being (EWB) known in Western countries. Using a mixed-methods and exploratory approach, including interviews with 17 older adults and the collection of 100 survey responses, we explored how older adults in the US experience a sense of meaning, and if and how a social robot could be used to help foster this sense. We find that meaning for older adults is often obtained by helping others, through family connections, and/or through activities of daily life, and that sources of meaning often differ based on the older adults’ living situation. Assessing how meaning compares to happiness and social connection, we highlight general similarities and differences, and also find that living situation influences older adults’ sources of happiness, desire for social connection, and barriers to well-being, in addition to companionship and happiness having a weaker correlation with meaning for those who live alone than for those who live with others. Additionally, we evaluated initial perceptions of a social robot (QT) meant to enhance ikigai and overall well-being, finding mostly positive perceptions, though those who live alone also reported being less willing to adopt a social robot into their homes. Using both data collected on older adults’ meaning and the potential use of QT to support meaning, we make several design recommendations with regards to using robots to enhance ikigai, such as by prompting daily reflecting, enhancing family bonds, and suggesting new experiences and volunteer opportunities.

Recent high-profile failures of domestic robotic products suggest more research is needed on factors that impact consumers' willingness to purchase robots, and the success rates of the consumer robotics industry compared with other innovative technologies. Using data from two crowdfunding sites (Kickstarter and Indiegogo), we summarize the applications, forms, prices, contexts of use, target populations, and sociality of potential consumer and home robots. We then use statistical analysis, predictive modeling, and word co-occurrence to determine which characteristics are associated with increased product support by early market consumers, finding that health and fitness, security and monitoring, and general education applications, cartoon-like and animal-like robot forms, and single user group robots have significantly more backers. We also find that social robots have a mean of 1.2–3.2 times as many backers as non-social robots and that every twofold increase in price results in a 20% decrease in financial supporters. Product reviews from these sites are additionally used to identify product features consumers found important. Finally, analyses of the failure rates of social and home robots find that these products are not failing more frequently than other innovative products overall. This research is among the first to study factors influencing consumers' purchasing behavior of home robots, and to use data mining methods to gain insights into home and consumer robot design. [ABSTRACT FROM AUTHOR]

Due to pandemic situations, the development of home robots that can make the house an optimized space for various activities is active. This study aims to confirm the effectiveness of approach or avoidance behavior for feedback positioning on the user experience, depending on the context in which the robot performs the task. Based on two types of the task contexts(Reactive vs. Proactive) and three types of robot feedback positioning(No move vs. Avoidance vs. Approach), six different scenarios were designed for experimental study. Likeability, perceived intelligence, rapport, negative attitude and predictability of behavior are measured for each conditions. The result showed the main effects of perceived intelligence, rapport, predictability in the context of tasks, and of likability, perceived intelligence, rapport in robot feedback positioning. The interaction effects were shown in likeability and perceived intelligence. In conclusion, approach-avoidance experiences can also be applied to robot behaviors as well, and the negative effects of avoidance have been significantly confirmed. [ABSTRACT FROM AUTHOR]

A wave of social, domestic robots is poised to enter our homes. Robots such as Jibo, Kuri and Olly are networked with other 'smart' devices and use cameras and voice control to provide companionship, care and household management. These robots are proposed as members of the family, and as such must encourage intimacy and trust with their human caregivers. In this article, we explore the nexus between the cute aesthetic of home robots and the kinds of affective relationships this aesthetic enables with the human user. Our argument is that the cuteness of home robots creates a highly ambivalent relationship of power between (human) subject and (robotic/digital) object, whereby the manifestation of consciousness and the production of lasting emotional bonds require home robots to exceed the affective and semiotic limitations, even as their cute appearance may encourage the production of intimacy. By exceeding the borders established by their own design, home robots are able to manifest as conscious beings, a manifestation which both destabilizes the power differential between user and robot and, paradoxically, points to the possibility of their own replacement. To explore these ideas, we discuss three soon-to-be-released social robots: Mayfield Robotics' Kuri, Emotech's Olly and Jibo Inc.'s Jibo. Each promises a unique personality that will integrate them as a member of the family. [ABSTRACT FROM AUTHOR]

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Background: Ikigai (meaning or purpose in life) is a concept understood by most older adults in Japan. The term has also garnered international attention, with recent academic attempts to map it to concepts in the Western well-being literature. In addition, efforts to use social and home robots to increase well-being have grown; however, they have mostly focused on hedonic well-being (eg, increasing happiness and decreasing loneliness) rather than eudaimonic well-being (eg, fostering meaning or purpose in life)., Objective: First, we explored how Japanese older adults experience ikigai and relate these to concepts in the Western well-being literature. Second, we investigated how a home robot meant to promote ikigai is perceived by older adults., Methods: We used a mixed methods research design-including 20 interviews with older adults, a survey of 50 older adults, and 10 interviews with family caregivers. For interviews, we asked questions about older adults' sources of ikigai, happiness, and social support, along with their perception of the robot (QT). For surveys, a number of well-being scales were used, including 2 ikigai scales-ikigai-9 and K-1-and 6 Patient-Reported Outcomes Measurement Information System scales, measuring meaning and purpose, positive affect, satisfaction with participation in social roles, satisfaction with participation in discretionary social activities, companionship, and emotional support. Questions related to the perception and desired adoption of the robot and older adults' health status were also included., Results: Our results suggest that health is older adults' most common source of ikigai. Additionally, although self-rated health correlated moderately with ikigai and other well-being measures, reported physical limitation did not. As opposed to social roles (work and family), we found that ikigai is more strongly related to satisfaction with discretionary social activities (leisure, hobbies, and friends) for older adults. Moreover, we found that older adults' sources of ikigai included the eudaimonic aspects of vitality, positive relations with others, contribution, accomplishment, purpose, and personal growth, with the first 3 being most common, and the hedonic aspects of positive affect, life satisfaction, and lack of negative affect, with the first 2 being most common. However, the concept of ikigai was most related to eudaimonic well-being, specifically meaning in life, along the dimension of significance. Finally, we found that Japanese older adults have high expectations of a home robot for well-being, mentioning that it should support them in a multitude of ways before they would likely adopt it. However, we report that those with the highest levels of meaning, and satisfaction with their leisure life and friendships, may be most likely to adopt it., Conclusions: We outline several ways to improve the robot to increase its acceptance, such as improving its voice, adding functional features, and designing it to support multiple aspects of well-being., (©Natasha Randall, Waki Kamino, Swapna Joshi, Wei-Chu Chen, Long-Jing Hsu, Katherine M Tsui, Selma Šabanović. Originally published in JMIR Aging (https://aging.jmir.org), 04.10.2023.)

Visual features can help predict if a manipulation behavior will succeed at a given location. For example, the success of a behavior that flips light switches depends on the location of the switch. We present methods that enable a mobile manipulator to autonomously learn a function that takes an RGB image and a registered 3D point cloud as input and returns a 3D location at which a manipulation behavior is likely to succeed. With our methods, robots autonomously train a pair of support vector machine (SVM) classifiers by trying behaviors at locations in the world and observing the results. Our methods require a pair of manipulation behaviors that can change the state of the world between two sets (e.g., light switch up and light switch down), classifiers that detect when each behavior has been successful, and an initial hint as to where one of the behaviors will be successful. When given an image feature vector associated with a 3D location, a trained SVM predicts if the associated manipulation behavior will be successful at the 3D location. To evaluate our approach, we performed experiments with a PR2 robot from Willow Garage in a simulated home using behaviors that flip a light switch, push a rocker-type light switch, and operate a drawer. By using active learning, the robot efficiently learned SVMs that enabled it to consistently succeed at these tasks. After training, the robot also continued to learn in order to adapt in the event of failure. [ABSTRACT FROM AUTHOR]

Previous research in human-robot interaction has explored using robots to increase objective and hedonic aspects of well-being and quality of life, but there is no literature on how robots might be used to support eudaimonic aspects of well-being (such as meaning in life). A sense of meaning has been shown to positively affect health and longevity. We frame our study around the Japanese concept of ikigai , which is widely used with Japanese older adults to enhance their everyday lives, and is closely related to the concept of eudaimonic well-being (EWB) known in Western countries. Using a mixed-methods and exploratory approach, including interviews with 17 older adults and the collection of 100 survey responses, we explored how older adults in the US experience a sense of meaning, and if and how a social robot could be used to help foster this sense. We find that meaning for older adults is often obtained by helping others, through family connections, and/or through activities of daily life, and that sources of meaning often differ based on the older adults' living situation. Assessing how meaning compares to happiness and social connection, we highlight general similarities and differences, and also find that living situation influences older adults' sources of happiness, desire for social connection, and barriers to well-being, in addition to companionship and happiness having a weaker correlation with meaning for those who live alone than for those who live with others. Additionally, we evaluated initial perceptions of a social robot (QT) meant to enhance ikigai and overall well-being, finding mostly positive perceptions, though those who live alone also reported being less willing to adopt a social robot into their homes. Using both data collected on older adults' meaning and the potential use of QT to support meaning, we make several design recommendations with regards to using robots to enhance ikigai , such as by prompting daily reflecting, enhancing family bonds, and suggesting new experiences and volunteer opportunities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Randall, Joshi, Kamino, Hsu, Agnihotri, Li, Williamson, Tsui and Šabanović.)

This paper is concerned with multiple microphone-based sound source localization in home robot environments. For this purpose, we use the excitation source information to determine the time-delay between each two microphones from speech source when robot's name is called. Furthermore, we present a novel method to estimate the reliable localization angle from the obtained lime-delay values. Thus, it can be used as a core technique in conjunction with human-robot interaction that can naturally interact between human and robots in home robot applications. The experimental results on sound localization database revealed that the presented method showed a good performance in comparison with the conventional methods such as time difference of arrival (TDOA) and generalized cross correlation- phase transform (GCC-PHAT) in distant-varying, noise and reverberant environments. [ABSTRACT FROM AUTHOR]

It still remains a challenge for robots to obtain knowledge automatically for performing home services. In the human learning process, natural languages act as an outline in guiding human beings complete tasks. From this point, a conditional generation method transforming textual manipulation instructions into action sequences is proposed, to provide home robots with knowledge automatically and improve the service skills finally. Due to the limited learning ability of the generation model on understanding complex semantic information, we present a two-phase conditional generation strategy in which the action space is reduced at the syntax level before generating action sequences semantically. For representing action sequences effectively, functional labels (FLs) are designed according to the requirements of performing home services, to identify six relationships about objects and actions. In action sequence generation, reinforcement learning is employed to guide the action sequence generation by introducing hierarchical rewards related to a priori knowledge, semantic similarity, and action logic. Based on statistic learning, a priori knowledge is constructed by modeling the relationship about object co-occurrence, action collaboration, and action–object correlation. The semantic similarity with Semantic Role Labeling enables the similarity evaluation between textual sentences (inputs) and produced sequences (outputs). And action logic, represented by the verb sequence in instructions, guides the production of action sequences logically. Experimental results demonstrate that the proposed method can produce competitive action sequences from textual instructions, and produced action sequences can be applied to robot for performing services. [ABSTRACT FROM AUTHOR]