The ability to perform effective planning is crucial for building an
instruction-following agent. When navigating through a new environment, an
agent is challenged with (1) connecting the natural language instructions with
its progressively growing knowledge of the world; and (2) performing long-range
planning and decision making in the form of effective exploration and error
correction. Current methods are still limited on both fronts despite extensive
efforts. In this paper, we introduce the Evolving Graphical Planner (EGP), a
model that performs global planning for navigation based on raw sensory input.
The model dynamically constructs a graphical representation, generalizes the
action space to allow for more flexible decision making, and performs efficient
planning on a proxy graph representation. We evaluate our model on a
challenging Vision-and-Language Navigation (VLN) task with photorealistic
images and achieve superior performance compared to previous navigation
architectures. For instance, we achieve a 53% success rate on the test split of
the Room-to-Room navigation task through pure imitation learning, outperforming
previous navigation architectures by up to 5%
