Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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

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Book Description

This open 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). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.

This open book is licensed under a Creative Commons License (CC BY). You can download Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection ebook for free in PDF format (5.8 MB).

Book Details

Subject
Engineering and Technology
Publisher
Springer
Published
2020
Pages
149
Edition
1
Language
English
ISBN13
9789811562624
ISBN10
9811562628
ISBN13 Digital
9789811562631
ISBN10 Digital
9811562636
PDF Size
5.8 MB
License
CC BY

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