Detecting unsafe behavior in neural network imitation policies for caregiving robotics

This paper explores the application of imitation learning in caregiving robotics, aiming at addressing the increasing demand for automated assistance in caring for the elderly and disabled. While 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, which is 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 further research in integrating safety models into policy training, which is crucial for the reliable deployment of neural network policies in caregiving robotics.