Risk-Aware Decision Making in Service Robots to Minimize Risk of Patient Falls in Hospitals
This research addresses the critical problem of patient fall prevention in healthcare settings by enabling service robots to make risk-aware decisions, which is an incremental step towards safer human-robot interaction in hospitals.
This paper proposes a novel risk-aware planning framework for service robots to minimize patient fall risk in hospitals by providing an assistive device. The framework combines learning-based prediction with model-based control, demonstrating in simulations that it can plan interventions to avoid high fall score events.
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.