In this chapter, we take a Bayesian nonparametric approach in defining a prior on the hidden Markov model that allows for flexibility in addressing the problem of modeling the complex dynamics during robot manipulation task. At first, considering the underlying dynamics that can be well-modeled as a hidden discrete Markov process, but in which there is uncertainty about the cardinality of the state space. Through the use of the hierarchical Dirichlet process (HDP), one can examine an HMM with an unbounded number of possible states. Subsequently, the sticky HDP-HMM is investigated for allowing more robust learning of the complex dynamics through a learned bias by increasing the probability of self-transitions. Additionally, although the HDP-HMM and its sticky extension are very flexible time series models, they make a strong Markovian assumption that observations are conditionally independent given the discrete HMM state. This assumption is often insufficient for capturing the temporal dependencies of the observations in real data. To address this issue, we consider extensions of the sticky HDP-HMM for learning the switching dynamical processes with switching linear dynamical system. In the later chapters of this book, we will verify the performances in modeling mulitmodal time series and present the results of robot movement identification, anomaly monitoring, and anomaly diagnose.


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    Titel :

    Nonparametric Bayesian Modeling of Multimodal Time Series


    Beteiligte:
    Zhou, Xuefeng (Autor:in) / Wu, Hongmin (Autor:in) / Rojas, Juan (Autor:in) / Xu, Zhihao (Autor:in) / Li, Shuai (Autor:in)


    Erscheinungsdatum :

    2020-07-22


    Format / Umfang :

    18 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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