  "Search for" or "Navigate to"? When finding an object, the two choices always
come up in our subconscious mind. Before seeing the target, we search for the
target based on experience. After seeing the target, we remember the target
location and navigate to. However, recently methods in object navigation field
almost only consider using object association to enhance "search for" phase
while neglect the importance of "navigate to" phase. Therefore, this paper
proposes the dual adaptive thinking (DAT) method to flexibly adjust the
different thinking strategies at different navigation stages. Dual thinking
includes search thinking with the object association ability and navigation
thinking with the target location ability. To make the navigation thinking more
effective, we design the target-oriented memory graph (TOMG) to store
historical target information and the target-aware multi-scale aggregator
(TAMSA) to encode the relative target position. We assess our methods on the
AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method
reports 10.8%, 21.5% and 15.7% increase in success rate (SR), success weighted
by path length (SPL) and success weighted by navigation efficiency (SNE),
respectively.
