Search for or Navigate to? Dual Adaptive Thinking for Object Navigation

Search for" or "Navigate to"? When we find a specific object in an unknown environment, the two choices always arise in our subconscious mind. Before we see the target, we search for the target based on prior experience. After we have located the target, we remember the target location and navigate to this location. However, recent object navigation methods almost only consider using object association to enhance the "search for" phase while neglect the importance of the "navigate to" phase. Therefore, this paper proposes a dual adaptive thinking (DAT) method that flexibly adjusts the thinking strategies in different navigation stages. Dual thinking includes both search thinking according to the object association ability and navigation thinking according to the target location ability. To make the navigation thinking more effective, we design a target-oriented memory graph (TOMG) that stores historical target information and a target-aware multi-scale aggregator (TAMSA) that encodes the relative position of the target. We assess our methods on the AI2-Thor dataset. Compared with state-of-the-art (SOTA) methods, our approach achieves 10.8%, 21.5% and 15.7% increases in the success rate (SR), success weighted by path length (SPL) and success weighted by navigation efficiency (SNE), respectively.