  In robot navigation, generalizing quickly to unseen environments is
essential. Hierarchical methods inspired by human navigation have been
proposed, typically consisting of a high-level landmark proposer and a
low-level controller. However, these methods either require precise high-level
information to be given in advance or need to construct such guidance from
extensive interaction with the environment. In this work, we propose an
approach that leverages a rough 2-D map of the environment to navigate in novel
environments without requiring further learning. In particular, we introduce a
dynamic topological map that can be initialized from the rough 2-D map along
with a high-level planning approach for proposing reachable 2-D map patches of
the intermediate landmarks between the start and goal locations. To use
proposed 2-D patches, we train a deep generative model to generate intermediate
landmarks in observation space which are used as subgoals by low-level
goal-conditioned reinforcement learning. Importantly, because the low-level
controller is only trained with local behaviors (e.g. go across the
intersection, turn left at a corner) on existing environments, this framework
allows us to generalize to novel environments given only a rough 2-D map,
without requiring further learning. Experimental results demonstrate the
effectiveness of the proposed framework in both seen and novel environments.
