  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.
