Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

Robotics

This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM).

Zhou, Xuefeng
Wu, Hongmin
Rojas, Juan
Xu, Zhihao
Li, Shuai

https://directory.doabooks.org/handle/20.500.12854/26952

Springer Nature

2020

Wahyuni

http://creativecommons.org/licenses/by/4.0/

Pdf

English

Textbooks

DOI
10.1007/978-981-15-6263-1