In this paper, the application of imitation learning in caregiving robotics is explored, aiming at addressing the increasing demand for automated assistance in caring for the elderly and disabled. Leveraging advancements in deep learning and control algorithms, the study focuses on training neural network policies using offline demonstrations. A key challenge addressed is the "Policy Stopping" problem, crucial for enhancing safety in imitation learning-based policies, particularly diffusion policies. Novel solutions proposed include ensemble predictors and adaptations of the normalizing flow-based algorithm for early anomaly detection. Comparative evaluations against anomaly detection methods like VAE and Tran-AD demonstrate superior performance on assistive robotics benchmarks. The paper concludes by discussing the further research in integrating safety models into policy training, crucial for the reliable deployment of neural network policies in caregiving robotics.

翻译：本文探讨了模仿学习在照护机器人领域的应用，旨在应对老年人与残障人士照护中日益增长的自动化辅助需求。研究利用深度学习与控制算法的最新进展，重点通过离线演示数据训练神经网络策略。所解决的一个关键挑战是"策略停止"问题，这对于提升基于模仿学习的策略（特别是扩散策略）的安全性至关重要。提出的创新解决方案包括集成预测器以及基于标准化流算法的改进方案，用于早期异常检测。与变分自编码器及Tran-AD等异常检测方法的对比评估表明，该方法在辅助机器人基准测试中表现出更优性能。文章最后讨论了将安全模型整合到策略训练中的进一步研究方向，这对于神经网络策略在照护机器人中的可靠部署具有关键意义。