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Planning under uncertainty is a crucial capability for autonomous systems tooperate reliably in uncertain and dynamic environments. The concern of safetybecomes even more critical in healthcare settings where robots interact withhuman patients. In this paper, we propose a novel risk-aware planning frameworkto minimize the risk of falls by providing a patient with an assistive device.Our approach combines learning-based prediction with model-based control toplan for the fall prevention task. This provides advantages compared toend-to-end learning methods in which the robot's performance is limited tospecific scenarios, or purely model-based approaches that use relatively simplefunction approximators and are prone to high modeling errors. We comparevarious risk metrics and the results from simulated scenarios show that usingthe proposed cost function, the robot can plan interventions to avoid high fallscore events.

Risk-Aware Decision Making in Service Robots to Minimize Risk of Patient Falls in Hospitals