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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Veröffentlicht in:	IEEE robotics and automation letters Jg. 3; H. 3; S. 1544 - 1551
Hauptverfasser:	Daehyung Park, Hoshi, Yuuna, Kemp, Charles C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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