Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Xuefeng Zhou
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
by Xuefeng Zhou
Published
2020
Publisher
Springer Nature
Pages
137
ISBN-13
9789811562631
Description
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). 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.
Subjects
Probability Theory
Bayesian Estimation
Bayesian Inference with INLA
Statistical Inference
Modeling and Reasoning with Bayesian Networks
Bayesian Methods for Statistical Analysis
Frequently Asked Questions
How many pages are in Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection?
This edition of Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection has approximately 137 pages. Please note, this is an estimate and the exact page count can vary between hardcover, paperback, and e-book versions.
How long does it take to read Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection?
For most readers, Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection typically takes between 2h 51m and 1h 54m to complete. This is based on the book's length of approximately 34,250 words and common reading speeds.
Here's a detailed breakdown: • Continuous reading at 250 WPM: approximately 2h 17m of focused reading • Casual reading (30 minutes/day): you could finish in roughly 5 days • Estimated word count: 34,250 words
Your individual reading time will vary based on your personal reading pace, the amount of daily reading time, and your familiarity with the subject matter.
What is the word count of Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection?
The estimated word count for Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection is approximately 34,250 words. This figure is calculated using industry-standard methods that consider genre-specific word density patterns, typical formatting and layout characteristics, and standard words-per-page ratios for published books.
This is an approximation — actual word count may vary based on font size, formatting, edition, and the presence of illustrations or charts.
Who is the author of Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection?
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection was written by Xuefeng Zhou.
When was Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection published?
The publication date for this specific edition is 2020. The original work may have been published on a different date.