Standard architectures used in instruction following often struggle on novel compositions of subgoals (e.g. navigating to landmarks or picking up objects) observed during training. We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard, non-modular sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to novel subgoal compositions, as well as to environments unseen in training.
@article{arxiv.2010.12764,
title = {Modular Networks for Compositional Instruction Following},
author = {Rodolfo Corona and Daniel Fried and Coline Devin and Dan Klein and Trevor Darrell},
journal= {arXiv preprint arXiv:2010.12764},
year = {2021}
}