  Object Goal Navigation (ObjectNav) task is to navigate an agent to an object
category in unseen environments without a pre-built map. In this paper, we
solve this task by predicting the distance to the target using
semantically-related objects as cues. Based on the estimated distance to the
target object, our method directly choose optimal mid-term goals that are more
likely to have a shorter path to the target. Specifically, based on the learned
knowledge, our model takes a bird's-eye view semantic map as input, and
estimates the path length from the frontier map cells to the target object.
With the estimated distance map, the agent could simultaneously explore the
environment and navigate to the target objects based on a simple human-designed
strategy. Empirical results in visually realistic simulation environments show
that the proposed method outperforms a wide range of baselines on success rate
and efficiency. Real-robot experiment also demonstrates that our method
generalizes well to the real world. Video at
https://www.youtube.com/watch?v=R79pWVGFKS4
