Conditional variational auto-encoders showed improved anomaly detection abilities over standard variational auto-encoders in literature. This paper explores the effectiveness of robot pose conditioning on thermal anomaly detection in the context of electrical substations. We introduce a multi-modal conditional variational auto-encoder framework, capable of reconstructing thermal images and robot poses. It utilises a multi-objective loss function consisting of mean squared error image and pose reconstruction loss and Kullback-Leibler divergence. Orientation showed to be the most effective conditioning pose, in the context of anomaly detection. A well performing network effectively reconstructs the original assets based on the latent space representation, contains only slightly blurred reconstructions in cases of uncertainty, has a structured latent space as principal component analysis reveals and shows high separability between the distributions of the image reconstruction errors for normal and anomalous samples.
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