Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

Name: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Brand: Springer Verlag, Singapore
SKU: 9789811562624
Price: 276.54 RON
Availability: InStock

Autor: Hongmin Wu, Juan Rojas

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Legare: Copertă tare

Editura: Springer Verlag, Singapore

Disponibilitate: În depozitul extern

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276.54 lei

This open access book focuses on robot introspection, which has a direct impact on physical human-ro...

Informații despre carte

Autor

Hongmin Wu, Juan Rojas

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2020

Pagini

137

EAN

9789811562624

ISBN

9811562628

Enbook ID

32862165

Editura

Springer Verlag, Singapore

Greutate

454

Dimensiuni

155 x 235 x 14

Descriere completă

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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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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