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arXiv · 1806.04012

Hierarchy of GANs for learning embodied self-awareness model

Mahdyar Ravanbakhsh·Mohamad Baydoun·Damian Campo·Pablo Marin·David Martin·Lucio Marcenaro·Carlo S. Regazzoni
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Abstract

In recent years several architectures have been proposed to learn embodied agents complex self-awareness models. In this paper, dynamic incremental self-awareness (SA) models are proposed that allow experiences done by an agent to be modeled in a hierarchical fashion, starting from more simple situations to more structured ones. Each situation is learned from subsets of private agent perception data as a model capable to predict normal behaviors and detect abnormalities. Hierarchical SA models have been already proposed using low dimensional sensorial inputs. In this work, a hierarchical model is introduced by means of a cross-modal Generative Adversarial Networks (GANs) processing high dimensional visual data. Different levels of the GANs are detected in a self-supervised manner using GANs discriminators decision boundaries. Real experiments on semi-autonomous ground vehicles are presented.

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Mahdyar Ravanbakhsh, Mohamad Baydoun, Damian Campo, Pablo Marin, David Martin, Lucio Marcenaro, Carlo S. Regazzoni. 2018-06-08. Hierarchy of GANs for learning embodied self-awareness model. https://arxiv.org/abs/1806.04012

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