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The article focuses on the socially assistive robotics (SAR) which improved human-robot interaction (HRI) and social interaction in general. SAR serves as a solution to major social problems, which include growing population of older people, rising costs of medical care and education, and shortages of social programs for education and training of people with disabilities. HRI brings together engineering and social science, cognitive science, neuroscience, ethics, and other disciplines directly related to robotics.

Discusses the coordination and learning in multirobot systems. Challenges of the robotics domain; Assessment of the interaction dynamics between robots and their environment; Exploration of the biases and constraints introduced by the system designer. INSET: New directions for New Directions by James A. Hendler..

Intelligent, interactive systems provide assistance by facilitating social interactions rather than by automating physical tasks. [ABSTRACT FROM AUTHOR]

Socially assistive robotics (SAR) aims to address critical areas and gaps in care by automating supervision, coaching, motivation, and companionship aspects of one-on-one interactions with individuals from various large and growing populations, including stroke survivors, the elderly and individuals with dementia, and children with autism spectrum disorders (ASDs). This article examines the ethical challenges of SAR from three points of view (user, caregiver, and peer) using core principles from medical ethics (autonomy, beneficence, nonmaleficence, and justice) to determine how intended and unintended effects of SAR can impact the delivery of care. [ABSTRACT FROM PUBLISHER]

Quantity and quality of motor exploration are proposed to be fundamental for infant motor development. However, it is still not clear what types of motor exploration contribute to learning. To determine whether changes in quantity of leg movement and/or variability of leg acceleration are related to performance in a contingency learning task, twenty 6-8-month-old infants with typical development participated in a contingency learning task. During this task, a robot provided reinforcement when the infant's right leg peak acceleration was above an individualized threshold. The correlation coefficient between the infant's performance and the change in quantity of right leg movement, linear variability, and nonlinear variability of right leg movement acceleration from baseline were calculated. Simple linear regression and multiple linear regression were calculated to explain the contribution of each variable to the performance individually and collectively. We found significant correlation between the performance and the change in quantity of right leg movement (r = 0.86, p < 0.001), linear variability (r = 0.71, p < 0.001), and nonlinear variability (r = 0.62, p = 0.004) of right leg movement acceleration, respectively. However, multiple linear regression showed that only quantity and linear variability of leg movements were significant predicting factors for the performance ratio (p < 0.001, adjusted R2 = 0.94). These results indicated that the quantity of exploration and variable exploratory strategies could be critical for the motor learning process during infancy. [ABSTRACT FROM AUTHOR]

Unlabelled: Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally., (© Hannah R Lawrence, Renee A Schneider, Susan B Rubin, Maja J Matarić, Daniel J McDuff, Megan Jones Bell. Originally published in JMIR Mental Health (https://mental.jmir.org).)

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.

Socially assistive robotics (SAR) has great potential to provide accessible, affordable, and personalized therapeutic interventions for children with autism spectrum disorders (ASD). However, human-robot interaction (HRI) methods are still limited in their ability to autonomously recognize and respond to behavioral cues, especially in atypical users and everyday settings. This work applies supervised machine-learning algorithms to model user engagement in the context of long-term, in-home SAR interventions for children with ASD. Specifically, we present two types of engagement models for each user: (i) generalized models trained on data from different users and (ii) individualized models trained on an early subset of the user's data. The models achieved about 90% accuracy (AUROC) for post hoc binary classification of engagement, despite the high variance in data observed across users, sessions, and engagement states. Moreover, temporal patterns in model predictions could be used to reliably initiate reengagement actions at appropriate times. These results validate the feasibility and challenges of recognition and response to user disengagement in long-term, real-world HRI settings. The contributions of this work also inform the design of engaging and personalized HRI, especially for the ASD community., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)