  Recent work in Vision-and-Language Navigation (VLN) has presented two
environmental paradigms with differing realism -- the standard VLN setting
built on topological environments where navigation is abstracted away, and the
VLN-CE setting where agents must navigate continuous 3D environments using
low-level actions. Despite sharing the high-level task and even the underlying
instruction-path data, performance on VLN-CE lags behind VLN significantly. In
this work, we explore this gap by transferring an agent from the abstract
environment of VLN to the continuous environment of VLN-CE. We find that this
sim-2-sim transfer is highly effective, improving over the prior state of the
art in VLN-CE by +12% success rate. While this demonstrates the potential for
this direction, the transfer does not fully retain the original performance of
the agent in the abstract setting. We present a sequence of experiments to
identify what differences result in performance degradation, providing clear
directions for further improvement.
