Titlebook: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection; Xuefeng Zhou,Hongmin Wu,Shuai Li Book‘‘‘‘‘‘‘‘ 2020 The E

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Introduction to Robot Introspection,ospection. The current issues of robot introspection are also introduced, which including the complex task representation, anomaly monitoring, diagnoses and recovery by assessing the quality of multimodal sensory data during robot manipulation. The overall content of this book is presented at the en

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,Nonparametric Bayesian Method for Robot Anomaly Monitoring,kill 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

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Book‘‘‘‘‘‘‘‘ 2020 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 t

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,Nonparametric Bayesian Method for Robot Anomaly Monitoring,d-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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