Bed-to-wheelchair transferring is a ubiquitous activity of daily living (ADL), but especially challenging for caregiving robots with limited payloads. We develop a novel algorithm that leverages the presence of other assistive devices: a Hoyer sling and a wheelchair for coarse manipulation of heavy loads, alongside a robot arm for fine-grained manipulation of deformable objects (Hoyer sling straps). We instrument the Hoyer sling and wheelchair with actuators and sensors so that they can become intelligent agents in the algorithm. We then focus on one subtask of the transferring ADL-tying Hoyer sling straps to the sling bar-that exemplifies the challenges of transfer: multi-agent planning, deformable object manipulation, and generalization to varying hook shapes, sling materials, and care recipient bodies. To address these challenges, we propose CART-MPC, a novel algorithm based on turn-taking multi-agent model predictive control that uses a learned neural dynamics model for a keypoint-based representation of the deformable Hoyer sling strap, and a novel cost function that leverages linking numbers from knot theory and neural amortization to accelerate inference. We validate it in both RCareWorld [1] simulation and real-world environments. In simulation, CART-MPC successfully generalizes across diverse hook designs, sling materials, and care recipient body shapes. In the real world, we show zero-shot sim-to-real generalization capabilities to tie deformable Hoyer sling straps on a sling bar towards transferring a manikin from a hospital bed to a wheelchair. See our websitefor supplementary materials: https://emprise.cs.cornell.edu/cart-mpc/.

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.

Mobile telepresence robots can help reduce loneliness by facilitating people to visit each other and have more social presence than visiting via video or audio calls. However, using new technology can be challenging for many older adults. In this article, we examine how older adults use and want to use mobile telepresence robots, how these robots affect their social connection, and how they can be improved for older adults’ use. We placed a mobile telepresence robot in the home of older adult primary participants ( N = 7; age 60 \(+\) ) for 7 months and facilitated monthly activities between them and a secondary participant ( N = 8; age 18 \(+\) ) of their choice. Participants used the robots as they liked between monthly activities. We collected diary entries and monthly interviews from primary participants and a final interview from secondary participants. Results indicate that older adults found many creative uses for the robots, including conversations, board games, and hide “n” seek. Several participants felt more socially connected with others and a few had improved their comfort with technology because of their use of the robot. They also suggested design recommendations and updates for the robots related to size, mobility, and more, which can help practitioners improve robots for older adults’ use.

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.

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.

There is increasingly heavy reliance on online social communication methods for reducing the sense of social isolation. However, this type of communication lacks one of the most critical elements of expressing emotion to comfort people: physical contact. In the current work, we examined people's perceptions of robots with an affective touch function for conveying sympathy from another person. We conducted two online studies to investigate how individuals evaluate imagined robot touch gestures sent to a friend from them (Study 1) and received by them from a friend (Study 2) in the United States and Japan. We found that sympathy senders preferred robot patting or rubbing the shoulder more than other types of touch, but would rather express sympathy through text or GIFs than by robot-mediated touch. In contrast, recipients perceived more sympathy and social support when they received robot touch gestures showing sympathy, compared to some other means to convey sympathy, short text in particular. The current findings highlight the different perspectives of senders and recipients on robot affective touch and potential cultural and individual differences in evaluations of robot affective touch.

Many people, particularly older adults, are negatively affected by social isolation and loneliness. Robots can facilitate social interaction between persons. In this on-going study, we interview middle and older adults in the US ($n=20$) and Japan ($n=4$) regarding their opinions of socially facilitative robots and how they want robots to assist in their social lives. Participants' desires for robots included robots that could act as an embodied avatar, teleconferencing robots that would be mobile and hands-free, and automated management of their social schedule. Some participants were opposed to the idea of socially facilitative robots, expressed concern regarding robot involvement in human social life, felt that robots were incapable of promoting social interactions between humans, or felt that robots were only for the very elderly and disabled and could not currently assist them. Emerging differences include that thus far, only US participants expressed a desire for pet robots or organizational robots, and all four current Japanese participants expressed the belief that robots were for the very elderly and disabled.

Children with insufficient exposure to language during critical developmental periods in infancy are at risk for cognitive, language, and social deficits [55]. This is especially difficult for deaf infants, as more than 90% are born to hearing parents with little sign language experience [48]. We created an integrated multi-agent system involving a robot and virtual human designed to augment language exposure for 6-12 month old infants. Human-machine design for infants is challenging, as most screen-based media are unlikely to support learning in [33]. While presently, robots are incapable of the dexterity and expressiveness required for signing, even if it existed, developmental questions remain about the capacity for language from artificial agents to engage infants. Here we engineered the robot and avatar to provide visual language to effect socially contingent human conversational exchange. We demonstrate the successful engagement of our technology through case studies of deaf and hearing infants.

A significant amount of research has been conducted regarding the technical aspects of haptic feedback. However, the design of effective haptic feedback behaviors for controlling ground-based mobile robots is not yet well understood from a human-robot interaction perspective. Past research of haptic feedback behaviors for mobile robots has sometimes made use of control paradigms that do not appropriately map to teleoperation or supervision tasks. Furthermore, evaluation of haptic behaviors has not been systematic and often only demonstrates feasibility. As a result, comparing various techniques is difficult. In this article, we focus on how haptic control research could be improved in the domain of teleoperation and supervision of ground-based mobile robots through the introduction of a haptic evaluation toolkit.

This year's human-robot interaction (HRI) conference focuses on "robots in the loop" and how robots are capable of enhancing the experiences of human users in everyday life and work. Telepresence robots allow operators the ability to participate in remote locations through their mobility and live bidirectional audio and video feeds. Using robotic telepresence, students with chronic illnesses are attending their regular classes, physicians are conducting virtual "home visits" for recovering patients, and remote teammates are having conversations beyond the office conference room.

Telepresence robots can be thought of as embodied video conferencing on wheels. Companies producing these robots imagine them being used in a wide variety of situations (e.g., ad-hoc conversations at the office, inspections and troubleshooting at factories, and patient rounds at medical facilities). In July and August 2010, we examined office-related use cases in a series of studies using two prototype robots (Anybots' QB and VGo Communications' VGo). In this paper, we present two studies: conference room meetings (n=6) and moving hallway conversations (n=24). We discuss who might benefit from using telepresence robots, in what scenarios, and the features that telepresence robots must incorporate for use in ad-hoc interactions.

Controlling the movements of mobile robots, including driving the robot through the world and panning the robot's cameras, typically requires many physical joysticks, buttons, and switches. Operators will often employ a technique called "chording" to cope with this situation. Much like a piano player, the operator will simultaneously actuate multiple joysticks and switches with his or her hands to create a combination of complimentary movements. However, these controls are in fixed locations and unable to be reprogrammed easily. Using a Microsoft Surface multi-touch table, we have designed an interface that allows chording and simultaneous multi-handed interaction anywhere that the user wishes to place his or her hands. Taking inspiration from the biomechanics of the human hand, we have created a dynamically resizing, ergonomic, and multi-touch controller (the DREAM Controller). This paper presents the design and testing of this controller with an iRobot ATRV-JR robot.

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We examine affective vocalizations provided by human teachers to robotic learners. In unscripted one-on-one interactions, participants provided vocal input to a robotic dinosaur as the robot selected toy buildings to knock down. We find that (1) people vary their vocal input depending on the learner's performance history, (2) people do not wait until a robotic learner completes an action before they provide input and (3) people naively and spontaneously use intensely affective prosody. Our findings suggest modifications may be needed to traditional machine learning models to better fit observed human tendencies. Our observations of human behavior contradict the popular assumptions made by machine learning algorithms (in particular, reinforcement learning) that the reward function is stationary and path-independent for social learning interactions.

Accessibility is a challenge for people with disabilities. Differences in cognitive ability, sensory impairments, motor dexterity, behavioral skills, and social skills must be taken into account when designing interfaces for assistive devices. Flexible interfaces tuned for individuals, instead of custom-built solutions, may benefit a larger number of people. The development and evaluation of a flexible interface for controlling a wheelchair mounted robotic arm is described in this paper. There are four versions of the interface based on input device (touch screen or joystick) and a moving or stationary shoulder camera. We describe results from an eight week experiment conducted with representative end users who range in physical and cognitive ability.