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