Tracking for people living with dementia and their care partners is primarily focused on quantified dementia symptoms presented to care partners. However, what people living with dementia want to track, what other aspects of dementia care partners wish to know, and how tracking fits within the care relationship remain to be identified. We performed an exploratory study in which eight people living with dementia and nine care partners provided iterative design feedback on a system concept: one that captures conversational data from a robot and visualizes it through a speech-tracking mobile application. Through reflexive thematic analysis, we found that people living with dementia wanted to use the system to maintain autonomy, especially by talking about their symptoms with the robot and using tracked information as a memory aid. Care partners valued numerical insights into the cognitive progress of people living with dementia only when accompanied by clear calls to action that supported them in their caregiver roles. Simultaneously, in their relational roles as spouses or children, care partners valued tracking memories and discussion points to understand their loved ones better. Our results suggest that providing related but distinct information tailored to each user’s needs can support both in their care relationship.

As robots achieve product longevity and increasingly enter intimate spaces of human life, from healthcare facilities to homes, they inevitably encounter contexts involving death, dying, grief, and loss. Nevertheless, mortality as a fundamental relational dimension has received limited systematic attention within Human-Robot Interaction (HRI) research. Drawing from empirical vignettes across multiple research projects, from robots at deathbeds and participants who passed away, to owners planning for robots they will leave behind, we demonstrate how death already surfaces in HRI, even when not designed for it. Through analysis of these encounters, we develop a framework for mortality-aware HRI that identifies key dimensions for designing and researching robots that acknowledge human and robotic finitude. We argue that as robots achieve long-term integration into people's lives, creating intertwined lifespans of humans and machines, the field must move beyond treating mortality as an edge case and instead recognize it as a core principle shaping how people relate to and make meaning with robots.

Human-Robot Interaction (HRI) research in real-world settings may lead to unanticipated, emotionally charged moments. While impacts of these moments on participants are reported in the literature, researchers’ emotions, which can affect participants’ experiences, are often left unreported. Learning from these moments is essential for advancing HRI quality and real-world deployment success. We introduce "Emotional Entanglements" as a lens in HRI to define a researcher's capacity to anticipate, absorb, respond to, and recover from emotionally impactful events. Collecting testimonials using collaborative autoethnography from eleven researchers, we surface recurring emotional entanglements experienced in HRI studies, including tears with mixed meaning, participant attachment and loss upon robot withdrawal, and consequential participant decisions attributed to the robot, as well as how researchers navigated them amidst protocol constraints. This paper provides an actionable guide to "Emotional Sustainability in HRI", raising awareness of these often unreported situations and offering strategies for mitigation.

As the global population ages, Human-Robot interaction (HRI) design is evolving from earlier perspectives that viewed older adults primarily as “recipients“ of help, to a more inclusive approach that challenges ableism and embraces active aging. In my work, I extend this concept by positioning older adults as active “carers,“ designing a robot to help them explore, understand, and reflect on their meaning and purpose in life-ikigai. Moreover, the co-design process empowers older adults as active participants, enabling them to achieve their values through the research process, whether by fostering well-being or providing educational benefits, guided by the approach of “research as CARe.“ In the future, I aim to enhance this work by co-designing for individual aging changes within a community context and advancing the co-design process for older adults.

Caregivers of older adults often undertake their caregiving journey driven by filial obligation, facing inherent expectations and multifaceted roles. While Human-Computer Interaction (HCI) research has explored these roles, some invisible work in managing them remains under-examined. To address this gap, we interviewed 19 informal caregivers of older adults to uncover their invisible work and the potential role of technology in supporting these complex responsibilities. Our findings detail the caregivers’ lived experiences, highlighting the challenges and strategies they employ in managing multiple roles. We discuss design opportunities that include facilitating the identification and reflection on existing roles, leveraging this understanding for coordination, aiding in role-based scheduling with acknowledgment, and providing support for the dynamic roles transitioning between various responsibilities. These insights could inform future caregiving technology design, enhancing support for caregivers in their multifaceted roles.

Participatory robot design projects with older adults often use multiple sessions to encourage design feedback and active participation from users. Prior projects have, however, not analyzed the learning outcomes for older adults across co-design sessions and how they support constructive design feedback and meaningful participation. To bridge this gap, we examined the learning outcomes within a "longitudinal panel." This panel comprised seven co-design sessions with 11 older adults of varying cognitive abilities over six months, aimed at designing a robot to guide a photograph-based conversational activity. Using Nelson and Stolterman's framework of the hierarchy of design-learning, we demonstrate how older adult panelists achieved multiple design-learning outcomes- capacity, confidence, capability, competence, courage, and connection- which allowed them to provide actionable design suggestions. We provide guidelines for conducting longitudinal panels that can enhance user design-learning and participation in robot design.

The sources of a person’s ikigai—their sense of meaning and purpose in life—often change as they age. Reflecting on past and new sources of ikigai may help people renew their sense of meaning as their life circumstances shift. Building on insights from an initial Wizard-of-Oz robot prototype [1], we describe the design of an autonomous robot that uses a semi-structured conversation format to help older adults reflect on what gives their life meaning and purpose. The robot uses both pre-determined (scripted) and Large Language Model (LLM) generated questions to personalize conversations with older adults around themes of social interaction, planning, accomplishments, goal setting, and the recent past. We evaluated the autonomous robot with 19 older adult participants in a lab setting and at two eldercare facilities. Analysis of the older adults’ conversations with the robot and their responses to an evaluative survey allowed us to identify several design considerations for an autonomous robot that can support ikigai reflection. Interweaving simple yet detailed predetermined questions with LLM-generated follow-up questions yielded enjoyable, in-depth conversations with older adults. We also recognized the need for the robot to be able to offer relevant suggestions when participants cannot recall events and people they find meaningful. These findings aim to further refine the design of an interactive robot that can support users in their exploration of life’s purpose.

Conventional co-design methods, such as storyboarding and focus groups, are not always appropriate for people living with dementia (PLwD). In pilot robot co-design workshops in a local memory care facility, we noticed PLwD struggled to understand, express themselves, fully participate, and benefit from the experience. After reflecting on challenges with the facility’s director of program development and education, we redesigned the workshops prioritizing elements of the Eden Alternative’s well-being for PLwD: identity, connectedness, security, and autonomy. We delivered these new workshops over five weeks with 12 PLwD participants. Analysis of resulting video recordings and transcripts shows the new activities allowed participants to see themselves as having knowledge relevant to social robot design; to relate to each other, the robot, and the researchers; to feel comfortable; and to actively contribute to and offer valuable insights for robot design. Participants reported feeling meaning, growth, and joy during the workshops.

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.

In imagining a future where climate change forces more intimate relationships between humans and nature, social robots can be introduced to revolutionize the way humans understand and communicate with plants. Through the Double Diamond design method with plant owners, we uncovered dif-ferent perspectives of the plant caretaking process and designed a social agent that aids in plant caretaking while fostering a positive human-robot interaction. Then, based on our takeaways from the design process, we crafted a story of an individual who interacts with a future version of our robot who overcomes the language barrier between plants and humans.