College students often face academic and life stressors affecting productivity, especially students with Attention Deficit Hyperactivity Disorder (ADHD) who experience executive functioning challenges. Conventional productivity tools typically demand sustained self-discipline and consistent use, which many students struggle with, leading to disruptive app-switching behaviors. Socially Assistive Robots (SARs), known for their intuitive and interactive nature, offer promising potential to support productivity in academic environments, having been successfully utilized in domains like education, cognitive development, and mental health. To leverage SARs effectively in addressing student productivity, this study employed a Participatory Design (PD) approach, directly involving college students and a Student Success and Well-Being Coach in the design process. Through interviews and a collaborative workshop, we gathered detailed insights on productivity challenges and identified desirable features for a productivity-focused SAR. Importantly, ethical considerations were integrated from the onset, facilitating responsible and user-aligned design choices. Our contributions include comprehensive insights into student productivity challenges, SAR design preferences, and actionable recommendations for effective robot characteristics. Additionally, we present stakeholder-derived ethical guidelines to inform responsible future implementations of productivity-focused SARs in higher education.

In the study, the device of social robot was designed for visually impaired users, and along with a mobile application for provide functions to assist their lives. Both physical and mental conditions of visually impaired users are considered, and the mobile application provides functions: photo record, mood lift, greeting guest and today highlight. The application was designed for visually impaired users, and uses voice control to provide a friendly interface. Photo record function allows visually impaired users to capture image immediately when they encounter danger situations. Mood lift function accompanies visually impaired users by asking questions, playing music and reading articles. Greeting guest function answers to the visitors for the inconvenient physical condition of visually impaired users. In addition, today highlight function read news including weather forecast, daily horoscopes and daily reminder for visually impaired users. Multiple tools were adopted for developing the mobile application, and a website was developed for caregivers to check statues of visually impaired users and for marketing of the application.

As social robots get more deeply integrated intoour everyday lives, they will be expected to engage in meaningful conversations and exhibit socio-emotionally intelligent listening behaviors when interacting with people. Active listening and backchanneling could be one way to enhance robots' communicative capabilities and enhance their effectiveness in eliciting deeper self-disclosure, providing a sense of empathy,and forming positive rapport and relationships with people.Thus, we developed an LLM-powered social robot that can exhibit contextually appropriate sentiment-based backchannelingand active listening behaviors (active listening+backchanneling) and compared its efficacy in eliciting people's self-disclosurein comparison to robots that do not exhibit any of these listening behaviors (control) and a robot that only exhibitsbackchanneling behavior (backchanneling-only). Through ourexperimental study with sixty-five participants, we found theparticipants who conversed with the active listening robot per-ceived the interactions more positively, in which they exhibited the highest self-disclosures, and reported the strongest senseof being listened to. The results of our study suggest that the implementation of active listening behaviors in social robotshas the potential to improve human-robot communication andcould further contribute to the building of deeper human-robot relationships and rapport.

As social robots increasingly enter public environments, their acceptance depends not only on technical reliability but also on ethical integrity, accessibility, and user trust. This paper reports on a pilot deployment of an ARI social robot functioning as a university receptionist, designed in alignment with the SecuRoPS framework for secure and ethical social robot deployment. Thirty-five students and staff interacted with the robot and provided structured feedback on safety, privacy, usability, accessibility, and transparency. The results show generally positive perceptions of physical safety, data protection, and ethical behavior, while also highlighting challenges related to accessibility, inclusiveness, and dynamic interaction. Beyond the empirical findings, the study demonstrates how theoretical frameworks for ethical and secure design can be implemented in real-world contexts through end-user evaluation. It also provides a public GitHub repository containing reusable templates for ARI robot applications to support reproducibility and lower the entry barrier for new researchers. By combining user perspectives with practical technical resources, this work contributes to ongoing discussions in AI and society and supports the development of trustworthy, inclusive, and ethically responsible social robots for public spaces.

The paper asserts that emulating empathy in human-robot interaction is a key component to achieve satisfying social, trustworthy, and ethical robot interaction with older people. Following comments from older adult study participants, the paper identifies a gap. Despite the acceptance of robot care scenarios, participants expressed the poor quality of the social aspect. Current human-robot designs, to a certain extent, neglect to include empathy as a theorized design pathway. Using rhetorical theory, this paper defines the socio-cultural expectations for convincing empathetic relationships. It analyzes and then summarizes how society understands, values, and negotiates empathic interaction between human companions in discursive exchanges, wherein empathy acts as a societal value system. Using two public research collections on robots, with one geared specifically to gerontechnology for older people, it substantiates the lack of attention to empathy in public materials produced by robot companies. This paper contends that using an empathetic care vocabulary as a design pathway is a productive underlying foundation for designing humanoid social robots that aim to support older people's goals of aging-in-place. It argues that the integration of affective AI into the sociotechnical assemblages of human-socially assistive robot interaction ought to be scrutinized to ensure it is based on genuine cultural values involving empathetic qualities.

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot capabilities and their potential to function as colleagues and companions are growing. This systematic literature review synthesises 22 empirical studies selected from an initial pool of 728 articles published between 2011 and 2024. Articles were retrieved from major scientific databases and identified based on empirical focus and conceptual relevance, namely, how to preserve and promote human autonomy and sense of agency in HRI. Derived through thematic synthesis, five clusters of potentially influential factors are revealed: robot adaptiveness, communication style, anthropomorphism, presence of a robot and individual differences. Measured through psychometric scales or the intentional binding paradigm, perceptions of autonomy and agency varied across industrial, educational, healthcare, care, and hospitality settings. The review underscores the theoretical differences between both concepts, but their yet entangled use in HRI. Despite increasing interest, the current body of empirical evidence remains limited and fragmented, underscoring the necessity for standardised definitions, more robust operationalisations, and further exploratory and qualitative research. By identifying existing gaps and highlighting emerging trends, this review contributes to the development of human-centered, autonomy-supportive robot design strategies that uphold ethical and psychological principles, ultimately supporting well-being in human-robot interaction.

For social robots to maintain long-term engagement as exercise instructors, rapport-building is essential. Motor mimicry--imitating one's physical actions--during social interaction has long been recognized as a powerful tool for fostering rapport, and it is widely used in rehabilitation exercises where patients mirror a physiotherapist or video demonstration. We developed a novel Brain-Robot Interface (BRI) that allows a social robot instructor to mimic a patient's exercise movements in real-time, using mental commands derived from the patient's intention. The system was evaluated in an exploratory study with 14 participants (3 physiotherapists and 11 hemiparetic patients recovering from stroke or other injuries). We found our system successfully demonstrated exercise mimicry in 12 sessions; however, accuracy varied. Participants had positive perceptions of the robot instructor, with high trust and acceptance levels, which were not affected by the introduction of BRI technology.

Abstract-- Trust and the perception of trustworthiness play an important role in decision-making and our behaviour towards others, and this is true not only of human-human interactions but also of human-robot interactions. While significant advances have been made in recent years in the field of social robotics, there is still some way to go before we fully understand the factors that influence human trust in robots. This paper presents the results of a study into the first impressions created by a social robot's facial features, based on the hypothesis that a'babyface' engenders trust. By manipulating the back-projected face of a Furhat robot, the study confirms that eye shape and size have a significant impact on the perception of trustworthiness. The work thus contributes to an understanding of the design choices that need to be made when developing social robots so as to optimise the effectiveness of human-robot interaction. Trust is a fundamental building block for any society to function properly.

Social robots are increasingly experimented in public and assistive settings, but their accessibility for Deaf users remains quite underexplored. Italian Sign Language (LIS) is a fully-fledged natural language that relies on complex manual and non-manual components. Enabling robots to communicate using LIS could foster more inclusive human robot interaction, especially in social environments such as hospitals, airports, or educational settings. This study investigates whether a commercial social robot, Pepper, can produce intelligible LIS signs and short signed LIS sentences. With the help of a Deaf student and his interpreter, an expert in LIS, we co-designed and implemented 52 LIS signs on Pepper using either manual animation techniques or a MATLAB based inverse kinematics solver. We conducted a exploratory user study involving 12 participants proficient in LIS, both Deaf and hearing. Participants completed a questionnaire featuring 15 single-choice video-based sign recognition tasks and 2 open-ended questions on short signed sentences. Results shows that the majority of isolated signs were recognized correctly, although full sentence recognition was significantly lower due to Pepper's limited articulation and temporal constraints. Our findings demonstrate that even commercially available social robots like Pepper can perform a subset of LIS signs intelligibly, offering some opportunities for a more inclusive interaction design. Future developments should address multi-modal enhancements (e.g., screen-based support or expressive avatars) and involve Deaf users in participatory design to refine robot expressivity and usability.

Contemporary robots are increasingly mimicking human social behaviours to facilitate interaction, such as smiling to signal approachability, or hesitating before taking an action to allow people time to react. Such techniques can activate a person's entrenched social instincts, triggering emotional responses as though they are interacting with a fellow human, and can prompt them to treat a robot as if it truly possesses the underlying life - like processes it outwardly presents, raising significant ethical questions. We engage these issues through the lens of informed consent: drawing upon pr evailing legal principles and ethics, we examine how social robots can influence user behaviour in novel ways, and whether under those circumstances users can be appropriately informed to consent to these heightened interactions. We explore the complex cir cumstances of human - robot interaction and highlight how it differ s from more familiar interaction contexts, and we apply legal principles relating to informed consent to social robots in order to reconceptualize the current ethical debates surrounding the field. From this investigation, we synthesize design goals for robot developers to achieve more ethical and informed human - robot interaction.