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Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments
[article]
2020
arXiv
pre-print
To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision. Multi-modal sensor signals could provide more information for such anomaly detection tasks; however, the fusion of high-dimensional and heterogeneous sensor modalities remains a challenging problem. We propose a deep learning neural network: supervised variational autoencoder (SVAE), for failure identification in unstructured and uncertain
arXiv:2012.08637v1
fatcat:yioxnxwktfdmtpceuk5zsdwicq