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.
翻译:在不确定情况下规划是自主系统在不确定和动态环境中可靠运作的关键能力。在机器人与人类病人互动的医疗保健环境中,安全关注变得更加重要。在本文件中,我们提出了一个新的风险意识规划框架,通过向病人提供辅助装置来尽量减少坠落风险。我们的方法将基于学习的预测与基于模型的控制结合起来,以规划秋季预防任务。这提供了优势,与终端到终端学习方法相比,即机器人的性能仅限于特定情景,或纯粹基于模型的方法,使用相对简单的功能近似器,并容易发生高建模错误。我们比较了各种风险指标和模拟情景的结果,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分事件。