  This work studies object goal navigation task, which involves navigating to
the closest object related to the given semantic category in unseen
environments. Recent works have shown significant achievements both in the
end-to-end Reinforcement Learning approach and modular systems, but need a big
step forward to be robust and optimal. We propose a hierarchical method that
incorporates standard task formulation and additional area knowledge as
landmarks, with a way to extract these landmarks. In a hierarchy, a low level
consists of separately trained algorithms to the most intuitive skills, and a
high level decides which skill is needed at this moment. With all proposed
solutions, we achieve a 0.75 success rate in a realistic Habitat simulator.
After a small stage of additional model training in a reconstructed virtual
area at a simulator, we successfully confirmed our results in a real-world
case.
