论文标题
服务机器人中的风险意识决策，以最大程度地减少医院患者跌倒的风险
Risk-Aware Decision Making in Service Robots to Minimize Risk of Patient Falls in Hospitals
论文作者
论文摘要
在不确定性下的计划是自主系统在不确定和动态环境中可靠运行的至关重要能力。在机器人与人类患者相互作用的医疗环境中，安全的关注变得更加重要。在本文中，我们提出了一个新颖的风险感知计划框架，以通过为患者提供辅助设备来最大程度地降低跌倒的风险。我们的方法将基于学习的预测与基于模型的控制结合在一起，以计划秋季预防任务。与端到端学习方法相比，这提供了优势，在端到端学习方法中，机器人的性能仅限于特定方案，或纯粹基于模型的方法，这些方法使用相对简单的函数近似器，并且容易出现高建模错误。我们比较了各种风险指标，以及模拟方案的结果表明，使用拟议的成本功能，机器人可以计划干预措施以避免高秋季分数事件。
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.
