  Vision-and-Language Navigation (VLN) is a natural language grounding task
where an agent learns to follow language instructions and navigate to specified
destinations in real-world environments. A key challenge is to recognize and
stop at the correct location, especially for complicated outdoor environments.
Existing methods treat the STOP action equally as other actions, which results
in undesirable behaviors that the agent often fails to stop at the destination
even though it might be on the right path. Therefore, we propose Learning to
Stop (L2Stop), a simple yet effective policy module that differentiates STOP
and other actions. Our approach achieves the new state of the art on a
challenging urban VLN dataset Touchdown, outperforming the baseline by 6.89%
(absolute improvement) on Success weighted by Edit Distance (SED).
