Socially assistive robots (SARs) have shown great promise in supplementing and augmenting interventions to support the physical and mental well-being of older adults. However, past work has not yet explored the potential of applying SAR to lower the barriers of long-term low vision rehabilitation (LVR) interventions for older adults. In this work, we present a user-informed design process to validate the motivation and identify major design principles for developing SAR for long-term LVR. To evaluate user-perceived usefulness and acceptance of SAR in this novel domain, we performed a two-phase study through user surveys. First, a group (n = 38) of older adults with LV completed a mailed-in survey. Next, a new group (n = 13) of older adults with LV saw an in-clinic SAR demo and then completed the survey. The study participants reported that SARs would be useful, trustworthy, easy to use, and enjoyable while providing socio-emotional support to augment LVR interventions. The in-clinic demo group reported significantly more positive opinions of the SAR's capabilities than did the baseline survey group that used mailed-in forms without the SAR demo.
As a new type of human companion, social robots are becoming more and more popular and expected to being fully integrated with human daily life in the near future. Being able to correctly perceive the emotions of users and react to it can increase the sense of trust, affinity, and social presence of human-robot interaction. In this paper, we propose a human-centered reinforcement learning strategy to train social robots to achieve autonomous emotion understanding and behavior shaping. Our whole study was conducted on the social robot Haru, which has a large library of routines to express different emotions. Our experimental results show that autonomous emotion understanding and behavior shaping of social robots can be achieved through continuous interaction with humans.
Social robots are increasingly used in learning settings. So far, the main focus of this has been in school lessons and teaching at universities. Another possible setting is the children’s hospital. There, for example, young patients need to acquire basic knowledge about their disease so that they can deal with it appropriately. This should be done in a joyful, fun way, as the situation is stressful enough in itself, and so learning is also facilitated. The paper presents a learning application for diabetic children that runs on Pepper. This social robot was particularly well suited for this task because it has a large integrated touchscreen, similar to a tablet. A learning game is displayed on it that was developed especially for this setting. The children have to estimate the carbohydrate values of foods and meals or answer knowledge questions. The social robot gives verbal and gestural feedback in each case. The subjects responded overwhelmingly positively to the learning application. Pepper’s visible and audible feedback plays a special role in this. Social robots like Pepper are an interesting solution for knowledge transfer in a children’s hospital.
As integrating social robots in elderly care scenarios becomes increasingly prevalent, the need for ethical decision-making frameworks to govern their actions is critically important. This paper presents a comprehensive computational approach using supervised machine learning algorithms to address the ethical considerations inherent in robot-assisted fetching tasks for the elderly. Drawing upon established ethical principles and novel moral dimensions specific to elderly care, we develop an intricate framework encompassing diverse entities and scenarios using a greet or beat approach. To validate the framework, we conducted a pilot study involving thirty participants experienced in caregiving. Through an interactive application, participants designed scenarios, decided whether the robot should fetch objects, and provided reasons for their choices. Their decisions were then compared with predictions generated by a set of machine learning algorithms trained on a dataset of various scenarios. Our results shed light on the diverse ethical perspectives in elderly care and the feasibility of automating ethical decision-making for social robots in this domain. This research contributes to the burgeoning field of roboethics, offering insights and tools to guide the responsible deployment of robots in assistive elderly care, ultimately promoting the well-being and ethical treatment of elderly individuals.
As the use of robotized products becomes more prevalent in real-world settings, users are confronted with unforeseen problematic situations. The response strategy of a robotized product can have a significant impact on mitigating negative perceptions of the product resulting from problematic situations. In this study, we introduce apology and risk communication strategies as potential response strategies for robotized products and explore their impact on user evaluations or attraction through experimental research. Our findings indicate that when a problem originates from the robotized product itself, the use of an apology strategy and an accommodative risk communication strategy can enhance users’ task attraction to the product, but do not affect their evaluation of the robot and social attraction to it. Conversely, when the problem is not caused by the robotized product, the use of a defensive risk communication strategy is associated with higher robot evaluation and greater user attraction compared to the use of an accommodative strategy. This study demonstrates that response strategies in problematic situations that people expect for robotized products are different from those for humans.
