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  1. Much research in healthcare robotics explores ex- tending rehabilitative interventions to the home. However, for adults, little guidance exists on how to translate human-delivered, clinic-based interventions into robot-delivered, home-based ones to support longitudinal interaction. This is particularly problematic for neurorehabilitation, where adults with cognitive impairments require unique styles of interaction to avoid frustration or overstimulation. In this paper, we address this gap by exploring the design of robot-delivered neurorehabilitation interventions for people with mild cognitive impairment (PwMCI). Through a multi-year collaboration with clinical neuropsychologists and PwMCI, we developed robot prototypes which deliver cognitive training at home. We used these prototypes as design probes to understand how participants envision long-term deployment of the intervention, and how it can be contextualized to the lives of PwMCI. We report our findings and specify design patterns and considerations for translating neurorehabilitation interventions to robots. This work will serve as a basis for future endeavors to translate cognitive training and other clinical interventions onto a robot, support longitudinal engagement with home-deployed robots, and ultimately extend the accessibility of longitudinal health interventions for people with cognitive impairments.
  2. Robots have great potential to support people with dementia (PwD) and their caregivers. They can provide support for daily living tasks, conduct household chores, provide companionship, and deliver cognitive stimulation and training. Personalizing these robots to an individual’s abilities and preferences can help enhance the quality of support they provide, increase their usability and acceptability, and alleviate caregiver burden. However, personalization can also introduce many risks, including risks to the safety and autonomy of PwD, the potential to exacerbate social isolation, and risks of being taken advantage of due to dark patterns in robot design. In this article, we weigh the risks and benefits by drawing on empirical data garnered from the existing ecosystem of robots used for dementia caregiving. We also explore ethical considerations for developing personalized cognitively assistive robots for PwD, including how a robot can practice beneficence to PwD, where responsibility falls when harm to a PwD occurs because of a robot, and how a robot can acquire informed consent from a PwD. We propose key technical and policy concepts to help robot designers, lawmakers, and others to develop personalized robots that protect users from unintended consequences, particularly for people with cognitive impairments.
  3. Robots have great potential to support people with dementia (PwD) and their caregivers. They can provide support for daily living tasks, conduct household chores, provide companionship, and deliver cognitive stimulation and training. Personalizing these robots to an individual’s abilities and preferences can help enhance the quality of support they provide, increase their usability and acceptability, and alleviate caregiver burden. However, personalization can also introduce many risks, including risks to the safety and autonomy of PwD, the potential to exacerbate social isolation, and risks of being taken advantage of due to dark patterns in robot design. In this article, we weigh the risks and benefits by drawing on empirical data garnered from the existing ecosystem of robots used for dementia caregiving. We also explore ethical considerations for developing personalized cognitively assistive robots for PwD, including how a robot can practice beneficence to PwD, where responsibility falls when harm to a PwD occurs because of a robot, and how a robot can acquire informed consent from a PwD. We propose key technical and policy concepts to help robot designers, lawmakers, and others to develop personalized robots that protect users from unintended consequences, particularly for people with cognitive impairments.
  4. The emergency department (ED) is a safety-critical environ- ment in which mistakes can be deadly and providers are over- burdened. Well-designed and contextualized robots could be an asset in the ED by relieving providers of non-value added tasks and enabling them to spend more time on patient care. To support future work in this application domain, in this paper, we characterize ED staff workflow and patient experience, and identify key considerations for robots in the ED, including safety, physical and behavioral attributes, usability, and training. Then, we discuss the task representation and data needed to situate the robot in the ED, based on this do- main knowledge. To the best of our knowledge, this is the first work on robot design for the ED that explicitly takes task acu- ity into account. This is an exciting area of research and we hope our work inspires further exploration into this problem domain.
  5. We investigate robust data aggregation in a multi-agent online learning setting. In reality, multiple online learning agents are often deployed to perform similar tasks and receive similar feedback. We study how agents can improve their collective performance by sharing information among each other. In this paper, we formulate the ε-multi-player multi-armed bandit problem, in which a set of M players that have similar reward distributions for each arm play concurrently. We develop an upper confidence bound-based algorithm that adaptively aggregates rewards collected by different players. To our best knowledge, we are the first to develop such a scheme in a multi-player bandit learning setting. We show that under the assumption that pairwise distances between the means of the player-dependent distributions for each arm are small, we improve the (collective) regret bound by nearly a factor of M , in comparison with a baseline algorithm in which the players learn individually using the UCB-1 algorithm (Auer et al., 2002). Our algorithm also exhibits a fallback guarantee, namely, if our task similarity assumption fails to hold, our algorithm still has a performance guarantee that cannot be worse than the baseline by a constant factor. Empirically, we validate our algorithm on synthetic data.