Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection - Info and Reading Options
By Xuefeng Zhou
"Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection" was published by Springer Nature in 2020 - Singapore and it has 137 pages.
“Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection” Metadata:
- Title: ➤ Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
- Author: Xuefeng Zhou
- Number of Pages: 137
- Publisher: Springer Nature
- Publish Date: 2020
- Publish Location: Singapore
“Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection” Subjects and Themes:
- Subjects: Bayesian inference - Robotics - Mathematical modelling - Automatic control engineering - Machine learning
Edition Specifications:
- Pagination: 1 electronic resource (137 p.)
Edition Identifiers:
- The Open Library ID: OL31373631M - OL23501349W
- ISBN-13: 9789811562631
- All ISBNs: 9789811562631
AI-generated Review of “Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection”:
"Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection" Description:
The Open Library:
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.
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