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.

Modular Networks for Compositional Instruction Following



<i>**Session Chair: ** Karthik Narasimhan </i><br><br>

3D-Oral: Language Grounding to Vision, Robotics and Beyond

technical paper

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Modular Networks for Compositional Instruction Following

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