  Natural language guided embodied task completion is a challenging problem
since it requires understanding natural language instructions, aligning them
with egocentric visual observations, and choosing appropriate actions to
execute in the environment to produce desired changes. We experiment with
augmenting a transformer model for this task with modules that effectively
utilize a wider field of view and learn to choose whether the next step
requires a navigation or manipulation action. We observed that the proposed
modules resulted in improved, and in fact state-of-the-art performance on an
unseen validation set of a popular benchmark dataset, ALFRED. However, our best
model selected using the unseen validation set underperforms on the unseen test
split of ALFRED, indicating that performance on the unseen validation set may
not in itself be a sufficient indicator of whether model improvements
generalize to unseen test sets. We highlight this result as we believe it may
be a wider phenomenon in machine learning tasks but primarily noticeable only
in benchmarks that limit evaluations on test splits, and highlights the need to
modify benchmark design to better account for variance in model performance.
