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Robots and other autonomous agents are well-positioned in the research discourse to support the care of people with challenges such as physical and/or cognitive disabilities. However, designing these robots can be complex as it involves considering a wide range of factors (e.g., individual needs, physical environment, technology capabilities, digital literacy), stakeholders (e.g., care recipients, formal and informal caregivers, technology developers), and contexts (e.g., hospitals, nursing homes, outpatient care facilities, private homes). The challenges are in gaining design insights for this unique use case and translating this knowledge into actionable, generalizable guidelines for other designers. This one-day workshop seeks to bring together researchers with diverse expertise and experience across academia, healthcare, and industry, spanning perspectives from multiple disciplines, including design, robotics, and human-computer interaction, with the primary goal being a consensus on best practices for generating and operationalizing design knowledge for robotic systems for care settings. 

Deploying socially assistive robots (SARs) at home, such as robotic companion pets, can be useful for tracking behavioral and health-related changes in humans during lifestyle fluctuations over time, like those experienced during CoVID-19. However, a fundamental problem required when deploying autonomous agents such as SARs in people’s everyday living spaces is understanding how users interact with those robots when not observed by researchers. One way to address that is to utilize novel modeling methods based on the robot’s sensor data, combined with newer types of interaction evaluation such as ecological momentary assessment (EMA), to recognize behavior modalities. This paper presents such a study of human-specific behavior classification based on data collected through EMA and sensors attached onboard a SAR, which was deployed in user homes. Classification was conducted using generative replay models, which attempt to use encoding/decoding methods to emulate how human dreaming is thought to create perturbations of the same experience in order to learn more efficiently from less data. Both multi-class and binary classification were explored for comparison, using several types of generative replay (variational autoencoders, generative adversarial networks, semi-supervised GANs). The highest-performing binary model showed approximately 79% accuracy (AUC 0.83), though multi-class classification across all modalities only attained 33% accuracy (AUC 0.62, F1 0.25), despite various attempts to improve it. The paper here highlights the strengths and weaknesses of using generative replay for modeling during human–robot interaction in the real world and also suggests a number of research paths for future improvement.