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...

Celý popis

Podrobná bibliografie

Vydáno v:	IEEE robotics and automation letters Ročník 3; číslo 3; s. 1544 - 1551
Hlavní autoři:	Daehyung Park, Hoshi, Yuuna, Kemp, Charles C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!