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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection (Paperback, 1st ed. 2020)
Loot Price: R1,372
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection (Paperback, 1st ed. 2020)
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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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