Detecting Unsafe Behavior in Neural Network Imitation Policies for Caregiving Robotics

TL;DR

This paper advances safety in caregiving robotics by developing novel anomaly detection methods for neural network imitation policies, improving early detection of unsafe behaviors to ensure reliable assistance for the elderly and disabled.

Contribution

It introduces ensemble predictors and flow-based anomaly detection techniques tailored for diffusion policies, enhancing safety in imitation learning for caregiving robots.

Findings

Outperforms VAE and Tran-AD in anomaly detection accuracy

Demonstrates improved safety in assistive robotics benchmarks

Provides a foundation for integrating safety models into policy training

Abstract

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.