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Tele-operated collaborative robots are used by many children for academic learning. However, as child-directed play is important for social-emotional learning, it is also important to understand how robots can facilitate play. In this article, we present findings from an analysis of a national, multi-year case study, where we explore how 53 children in grades K–12 (n= 53) used robots for self-directed play activities. The contributions of this article are as follows. First, we present empirical data on novel play scenarios that remote children created using their tele-operated robots. These play scenarios emerged in five categories of play: physical, verbal, visual, extracurricular, and wished-for play. Second, we identify two unique themes that emerged from the data—robot-mediated play as a foundational support of general friendships and as a foundational support of self-expression and identity. Third, our work found that robot-mediated play provided benefits similar to in-person play. Findings from our work will inform novel robot and HRI design for tele-operated and social robots that facilitate self-directed play. Findings will also inform future interdisciplinary studies on robot-mediated play. 

Many robot-delivered health interventions aim to support people longitudinally at home to complement or replace in-clinic treat- ments. However, there is little guidance on how robots can support collaborative goal setting (CGS). CGS is the process in which a person works with a clinician to set and modify their goals for care; it can improve treatment adherence and efficacy. However, for home-deployed robots, clinicians will have limited availability to help set and modify goals over time, which necessitates that robots support CGS on their own. In this work, we explore how robots can facilitate CGS in the context of our robot CARMEN (Cognitively Assistive Robot for Motivation and Neurorehabilitation), which delivers neurorehabilitation to people with mild cognitive impairment (PwMCI). We co-designed robot behaviors for supporting CGS with clinical neuropsychologists and PwMCI, and prototyped them on CARMEN. We present feedback on how PwMCI envision these behaviors supporting goal progress and motivation during an intervention. We report insights on how to support this process with home-deployed robots and propose a framework to support HRI researchers interested in exploring this both in the context of cognitively assistive robots and beyond. This work supports design- ing and implementing CGS on robots, which will ultimately extend the efficacy of robot-delivered health interventions. 

Clinical educators have used robotic and virtual patient simulator systems (RPS) for dozens of years, to help clinical learners (CL) gain key skills to help avoid future patient harm. These systems can simulate human physiological traits; however, they have static faces and lack the realistic depiction of facial cues, which limits CL engagement and immersion. In this article, we provide a detailed review of existing systems in use, as well as describe the possibilities for new technologies from the human–robot interaction and intelligent virtual agents communities to push forward the state of the art. We also discuss our own work in this area, including new approaches for facial recognition and synthesis on RPS systems, including the ability to realistically display patient facial cues such as pain and stroke. Finally, we discuss future research directions for the field.

 

11% of adults report experiencing cognitive decline which can im- pact memory, behavior, and physical abilities. Robots have great potential to support people with cognitive impairments, their caregivers, and clinicians by facilitating treatments such as cognitive neurorehabilitation. Personalizing these treatments to individual preferences and goals is critical to improving engagement and adherence, which helps improve treatment efficacy. In our work, we explore the efficacy of robot-assisted neurorehabilitation and aim to enable robots to adapt their behavior to people with cognitive impairments, a unique population whose preferences and abilities may change dramatically during treatment. Our work aims to en- able more engaging and personalized interactions between people and robots, which can profoundly impact robot-assisted treatment, how people receive care, and improve their everyday lives. 

An estimated 11% of adults report experiencing some form of cognitive decline which may be associated with conditions such as stroke or dementia, and can impact their memory, cognition, behavior, and physical abilities. While there are no known pharmacological treatments for many of these conditions, behavioral treatments such as cognitive training can prolong the independence of people with cognitive impairments. These treatments teach metacognitive strategies to compensate for memory difficulties in their everyday lives. Personalizing these treatments to suit the preferences and goals of an individual is critical to improving their engagement and sustainment, as well as maximizing the treatment’s effectiveness. Robots have great potential to facilitate these training regimens and support people with cognitive impairments, their caregivers, and clinicians. This article examines how robots can adapt their behavior to be personalized to an individual in the context of cognitive neurorehabilitation. We provide an overview of existing robots being used to support neurorehabilitation, and identify key principles to working in this space. We then examine state-of-the-art technical approaches to enabling longitudinal behavioral adaptation. To conclude, we discuss our recent work on enabling social robots to automatically adapt their behavior and explore open challenges for longitudinal behavior adaptation. This work will help guide the robotics community as they continue to provide more engaging, effective, and personalized interactions between people and robots. 

Dementia affects >50 million worldwide, causing progressive cognitive and physical disabilities. Its caregiving burden falls largely onto informal caregivers, who experience their own health problems, and face tremendous stress with little support–all exacerbated during COVID-19. In this paper, we present a new caregiver sup- port perspective, where the lenses of health equity and community health can shape future technology design. Through a 1.5 year long, in-depth research process with dementia community health workers, we learned how caregiving support technology can reflect key concepts in dementia community health practice. This paper makes two contributions: 1) We propose employing embodied cueing, such as imitation or action mimicry, as a communication modality that can align technology with community caregiving approaches, promote agency in people with dementia, and relieve caregiver burden, and 2) We suggest new avenues for HCI research to advance health equity in the context of dementia technology design. 

The robotics community continually strives to create robots that are deployable in real-world environments. Often, robots are expected to interact with human groups. To achieve this goal, we introduce a new method, the Robot-Centric Group Estimation Model (RoboGEM), which enables robots to detect groups of people. Much of the work reported in the literature focuses on dyadic interactions, leaving a gap in our understanding of how to build robots that can effectively team with larger groups of people. Moreover, many current methods rely on exocentric vision, where cameras and sensors are placed externally in the environment, rather than onboard the robot. Consequently, these methods are impractical for robots in unstructured, human-centric environments, which are novel and unpredictable. Furthermore, the majority of work on group perception is supervised, which can inhibit performance in real-world settings. RoboGEM addresses these gaps by being able to predict social groups solely from an egocentric perspective using color and depth (RGB-D) data. To achieve group predictions, RoboGEM leverages joint motion and proximity estimations. We evaluated RoboGEM against a challenging, egocentric, real-world dataset where both pedestrians and the robot are in motion simultaneously, and show RoboGEM outperformed two state-of-the-art supervised methods in detection accuracy by up to 30%, with a lower miss rate. Our work will be helpful to the robotics community, and serve as a milestone to building unsupervised systems that will enable robots to work with human groups in real-world environments.