  Little inquiry has explicitly addressed the role of action spaces in
language-guided visual navigation -- either in terms of its effect on
navigation success or the efficiency with which a robotic agent could execute
the resulting trajectory. Building on the recently released VLN-CE setting for
instruction following in continuous environments, we develop a class of
language-conditioned waypoint prediction networks to examine this question. We
vary the expressivity of these models to explore a spectrum between low-level
actions and continuous waypoint prediction. We measure task performance and
estimated execution time on a profiled LoCoBot robot. We find more expressive
models result in simpler, faster to execute trajectories, but lower-level
actions can achieve better navigation metrics by approximating shortest paths
better. Further, our models outperform prior work in VLN-CE and set a new
state-of-the-art on the public leaderboard -- increasing success rate by 4%
with our best model on this challenging task.
