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

TL;DR

This paper introduces a risk-aware planning framework for service robots in hospitals to effectively minimize patient fall risks by integrating learning-based prediction with model-based control, enhancing safety in healthcare environments.

Contribution

The paper presents a novel hybrid planning approach that combines learning and model-based control to improve fall prevention in healthcare robots.

Findings

The proposed method effectively reduces fall risk in simulated scenarios.

Risk metrics influence the robot's intervention planning.

Hybrid approach outperforms purely model-based or learning-based methods.

Abstract

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.