  People navigating in unfamiliar buildings take advantage of myriad visual,
spatial and semantic cues to efficiently achieve their navigation goals.
Towards equipping computational agents with similar capabilities, we introduce
Pathdreamer, a visual world model for agents navigating in novel indoor
environments. Given one or more previous visual observations, Pathdreamer
generates plausible high-resolution 360 visual observations (RGB, semantic
segmentation and depth) for viewpoints that have not been visited, in buildings
not seen during training. In regions of high uncertainty (e.g. predicting
around corners, imagining the contents of an unseen room), Pathdreamer can
predict diverse scenes, allowing an agent to sample multiple realistic outcomes
for a given trajectory. We demonstrate that Pathdreamer encodes useful and
accessible visual, spatial and semantic knowledge about human environments by
using it in the downstream task of Vision-and-Language Navigation (VLN).
Specifically, we show that planning ahead with Pathdreamer brings about half
the benefit of looking ahead at actual observations from unobserved parts of
the environment. We hope that Pathdreamer will help unlock model-based
approaches to challenging embodied navigation tasks such as navigating to
specified objects and VLN.
