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Multimodal sensory signals can be helpful for yuuna a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem for model-based anomaly detection.
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We introduce a long short-term memory-based variational autoencoder LSTM-VAE that fuses signals yuuna reconstructs their expected distribution by hoshi a progress-based varying prior. For evaluations with robot-assisted feeding executions, including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve of 0. We also show the variational autoencoding and state-based thresholding are effective in detecting anomalies from 17 raw sensory signals without significant feature engineering effort.
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