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Multimodal Representation Learning for Robotic Cross-Modality Policy Transfer

In this thesis, we aim at endowing robots with mechanisms to learn multimodal representations from sensory data and to allow them to execute tasks considering different subsets of available perceptions. We address the learning of these representations from supervised, unsupervised and reinforcement learning methodologies in the context of virtual agents and robots. We hope that, by achieving the proposed goals, the contributions of this thesis might prompt future research on applications of multimodal representations in robots and other artificial agents.