2018/06/08 by Mahdyar Ravanbakhsh, Ravanbakhsh, Mahdyar, Mohamad Baydoun +11
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Multimedia (cs.MM) #cs.CV #cs.MM
paper · pdf · doi:10.48550/arxiv.1806.04012
2018 IEEE International Conference on Image Processing - ICIP'18. arXiv admin note: text overlap with arXiv:1806.02609
arxiv created 2018/06/08 · arxiv updated 2018/06/12
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.