  VLN-CE is a recently released embodied task, where AI agents need to navigate
a freely traversable environment to reach a distant target location, given
language instructions. It poses great challenges due to the huge space of
possible strategies. Driven by the belief that the ability to anticipate the
consequences of future actions is crucial for the emergence of intelligent and
interpretable planning behavior, we propose DREAMWALKER -- a world model based
VLN-CE agent. The world model is built to summarize the visual, topological,
and dynamic properties of the complicated continuous environment into a
discrete, structured, and compact representation. DREAMWALKER can simulate and
evaluate possible plans entirely in such internal abstract world, before
executing costly actions. As opposed to existing model-free VLN-CE agents
simply making greedy decisions in the real world, which easily results in
shortsighted behaviors, DREAMWALKER is able to make strategic planning through
large amounts of ``mental experiments.'' Moreover, the imagined future
scenarios reflect our agent's intention, making its decision-making process
more transparent. Extensive experiments and ablation studies on VLN-CE dataset
confirm the effectiveness of the proposed approach and outline fruitful
directions for future work.
