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Visually grounded language learning for robot navigation
We present an end-to-end deep learning model for robot navigation from raw visual pixel input and natural text instructions. The proposed model is an LSTM-based sequence-to-sequence neural network architecture with attention, which is trained on instructionperception data samples collected in a synt
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