  Navigating unfamiliar environments presents significant challenges for blind
and low-vision (BLV) individuals. In this work, we construct a dataset of
images and goals across different scenarios such as searching through kitchens
or navigating outdoors. We then investigate how grounded instruction generation
methods can provide contextually-relevant navigational guidance to users in
these instances. Through a sighted user study, we demonstrate that large
pretrained language models can produce correct and useful instructions
perceived as beneficial for BLV users. We also conduct a survey and interview
with 4 BLV users and observe useful insights on preferences for different
instructions based on the scenario.
