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    Nonparametric Bayesian Method for Robot Anomaly Monitoring

    Nonparametric Bayesian Method for Robot Anomaly Monitoring

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    In this chapter, we introduce an anomaly monitoring pipeline using the Bayesian nonparametric hidden Markov models after the task representation and skill identification in previous chapter, which divided into three categories according to different thresholds definition, including (i) log-likelihood-based threshold, (ii) threshold based on the gradient of log-likelihood, and (iii) computing the threshold by mapping latent state to log-likelihood. Those method are effectively implement the anomaly monitoring during robot manipulation task. We also evaluate and analyse the performance and results for each method, respectively.

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    Titel :

    Nonparametric Bayesian Method for Robot Anomaly Monitoring

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

    Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection ; Kapitel : 4 ; 51-93

    Erscheinungsdatum :

    2020-07-22

    Format / Umfang :

    43 pages

    ISBN :

    978-981-15-6263-1 , 978-981-15-6262-4

    DOI :

    https://doi.org/10.1007/978-981-15-6263-1_4

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Engineering , Robotics and Automation , Bayesian Inference , Control, Robotics, Mechatronics , Machine Learning , Mathematical Modeling and Industrial Mathematics , Mathematics and Statistics

    Lizenzbestimmungen :

    https://creativecommons.org/licenses/by/4.0/

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