A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder

The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem for model-based...

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Vydané v:	IEEE robotics and automation letters Ročník 3; číslo 3; s. 1544 - 1551
Hlavní autori:	Daehyung Park, Hoshi, Yuuna, Kemp, Charles C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Anomalies Anomaly detection assistive robots Decoding deep learning in robotics and automation Detectors Failure detection and recovery Gaussian distribution Hazards Hidden Markov models Robot sensing systems Robots Sensors
ISSN:	2377-3766, 2377-3766
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