Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments

Tianchen Ji, Sri Theja Vuppala, Girish Chowdhary, Katherine Driggs-Campbell

Research output: Contribution to journalConference articlepeer-review

Abstract

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 environments. Our model leverages the representational power of VAE to extract robust features from high-dimensional inputs for supervised learning tasks. The training objective unifies the generative model and the discriminative model, thus making the learning a one-stage procedure. Our experiments on real field robot data demonstrate superior failure identification performance than baseline methods, and that our model learns interpretable representations.

Original languageEnglish (US)
Pages (from-to)1443-1455
Number of pages13
JournalProceedings of Machine Learning Research
Volume155
StatePublished - 2020
Event4th Conference on Robot Learning, CoRL 2020 - Virtual, Online, United States
Duration: Nov 16 2020Nov 18 2020

Keywords

  • Anomaly Detection
  • Feature Learning
  • Field Robots

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability

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