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In recent years several architectures have been proposed to learn embodiedagents complex self-awareness models. In this paper, dynamic incrementalself-awareness (SA) models are proposed that allow experiences done by an agentto be modeled in a hierarchical fashion, starting from more simple situationsto more structured ones. Each situation is learned from subsets of privateagent perception data as a model capable to predict normal behaviors and detectabnormalities. Hierarchical SA models have been already proposed using lowdimensional sensorial inputs. In this work, a hierarchical model is introducedby means of a cross-modal Generative Adversarial Networks (GANs) processinghigh dimensional visual data. Different levels of the GANs are detected in aself-supervised manner using GANs discriminators decision boundaries. Realexperiments on semi-autonomous ground vehicles are presented.

Hierarchy of GANs for learning embodied self-awareness model