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NONPARAMETRIC BAYESIAN LEARNING FOR COLLABORATIVE ROBOT MULT IBD

HONGMIN WU / JUAN ROJAS / XUEFENG ZHOU
SPRINGER
07 / 2020
9789811562648
Inglés

Sinopsis

This open access book focuses onárobot introspection,áwhicháhas a direct impact on physical human-robot interactionáandálong-term autonomy,áandáwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,áthe abilityátoáreason,ásolve their ownáanomaliesáand proactivelyáenrich 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 canáeffectivelyábe modeled as a parametricáhidden Markovámodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using theáhierarchical Dirichletáprocess (HDP) on the standard HMM parameters,áknown as theáHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andáallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is aávaluableáreferenceáresource foráresearchers and designers ináthe fieldáof robot learning and multimodal perception, as well as for senior undergraduate and graduateáuniversityástudents.