Fast, robust, and versatile event detection through HMM belief state gradient measures

Luo, Shuangqi; Wu, Hongmin; Lin, Hongbin; Duan, Shuangda; Guan, Yisheng; Rojas, Juan

doi:10.1109/roman.2018.8705268

Cited by 11 publications

(14 citation statements)

References 22 publications

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“…Anomaly identification sensitivity is intimately connected to threshold setting. We compare the results of our execution varying threshold with a fixed threshold presented in [35]. The comparison is seen in Fig.…”

Section: • Results and Analysis Of Anomaly Monitoring In Kitting Expementioning

confidence: 99%

Nonparametric Bayesian Method for Robot Anomaly Monitoring

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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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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Section: • Results and Analysis Of Anomaly Monitoring In Kitting Expementioning

confidence: 99%

Nonparametric Bayesian Method for Robot Anomaly Monitoring

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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“…As alternatives, probabilistic series-models, such as the Hidden Markov Model (HMM) [32] and the Gaussian Mixture Model (GMM) [33] are also applied to CDI by exploiting the dependence properties of time series. In [34], an HMM is developed to detect exceptional events in an object-alignment robot task, where the measured torques and their derivatives are used.…”

Section: Related Workmentioning

confidence: 99%

An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory

Zhang

Qian

Schuller

et al. 2021

IEEE Trans. Automat. Sci. Eng.

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proposed collision identification scheme in this paper especially focuses on overcoming the following two challenges in practice: firstly, to timely and accurately report a collision within its early stage; secondly, to ensure a high identification accuracy in a complicated environment, where ubiquitous disturbance and noise are unneglectable. The experimental validation at the end of this paper confirms its promising application value in humanrobot collaboration.

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Section: Anomaly Detection In Kitting Experimentsmentioning

confidence: 99%

“…For the anomaly detection, we achieved it by comparing the cumulative log-likelihood deviation to a given threshold from nominal executions [1,2,9]. An improved anomaly detector will be proposed in this paper, which can more effectively and robustly detect the anomalies.…”

Section: Introductionmentioning

confidence: 99%

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A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection

Guan

Rojas

2019

Applied Sciences

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Robot introspection is expected to greatly aid longer-term autonomy of autonomous manipulation systems. By equipping robots with abilities that allow them to assess the quality of their sensory data, robots can detect and classify anomalies and recover appropriately from common anomalies. This work builds on our previous Sense-Plan-Act-Introspect-Recover (SPAIR) system. We introduce an improved anomaly detector that exploits latent states to monitor anomaly occurrence when robots collaborate with humans in shared workspaces, but also present a multiclass classifier that is activated with anomaly detection. Both implementations are derived from Bayesian non-parametric methods with strong modeling capabilities for learning and inference of multivariate time series with complex and uncertain behavior patterns. In particular, we explore the use of a hierarchical Dirichlet stochastic process prior to learning a Hidden Markov Model (HMM) with a switching vector auto-regressive observation model (sHDP-VAR-HMM). The detector uses a dynamic log-likelihood threshold that varies by latent state for anomaly detection and the anomaly classifier is implemented by calculating the cumulative log-likelihood of testing observation based on trained models. The purpose of our work is to equip the robot with anomaly detection and anomaly classification for the full set of skills associated with a given manipulation task. We consider a human-robot cooperation task to verify our work and measure the robustness and accuracy of each skill. Our improved detector succeeded in detecting 136 common anomalies and 368 nominal executions with a total accuracy of 91.0%. An overall anomaly classification accuracy of 97.1% is derived by performing the anomaly classification on an anomaly dataset that consists of 7 kinds of detected anomalies from a total of 136 anomalies samples.

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Fast, robust, and versatile event detection through HMM belief state gradient measures

Cited by 11 publications

References 22 publications

Nonparametric Bayesian Method for Robot Anomaly Monitoring

Nonparametric Bayesian Method for Robot Anomaly Monitoring

An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory

A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection

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