Nonparametric Bayesian Method for Robot Anomaly Diagnose

Abstract

In this chapter, we introduce two novel anomaly diagnose methods using the Bayesian nonparametric hidden Markov models when anomaly triggered, including i)multi-class classifier based on nonparametric models, ii) sparse representation by statistical feature extraction for anomaly diagnose. Additionally, the detail procedure for anomaly sample definition, the supervised learning dataset collection as well as the data augmentation of insufficient samples are also declared. We evaluated the proposed methods with a multi-step human-robot collaboration objects kitting task on Baxter robot, the performance and results are presented of each method respectively.