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. Once we have seen the target, we can navigate to it by remembering the target location. However, recent object navigation methods consider using object association mostly to enhance the "search for" phase while neglecting the importance of the "navigate to" phase. Therefore, this paper proposes a dual adaptive thinking (DAT) method that flexibly adjusts 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 navigation thinking more effective, we design a target-oriented memory graph (TOMG) (which stores historical target information) and a target-aware multi-scale aggregator (TAMSA) (which encodes the relative position of the target). We assess our methods based on the AI2-Thor and RoboTHOR datasets. Compared with state-of-the-art (SOTA) methods, our approach significantly raises the overall success rate (SR) and success weighted by path length (SPL) while enhancing the agent’s performance in the "navigate to" phase.