  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. Videos of our results
are available on our website:
https://sites.google.com/illinois.edu/supervised-vae .
