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We propose to decompose instruction execution to goal prediction and actiongeneration. We design a model that maps raw visual observations to goals usingLINGUNET, a language-conditioned image generation network, and then generatesthe actions required to complete them. Our model is trained from demonstrationonly without external resources. To evaluate our approach, we introduce twobenchmarks for instruction following: LANI, a navigation task; and CHAI, wherean agent executes household instructions. Our evaluation demonstrates theadvantages of our model decomposition, and illustrates the challenges posed byour new benchmarks.

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction