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Direct search for objects as part of navigation poses a challenge for smallitems. Utilizing context in the form of object-object relationships enablehierarchical search for targets efficiently. Most of the current approachestend to directly incorporate sensory input into a reward-based learningapproach, without learning about object relationships in the naturalenvironment, and thus generalize poorly across domains. We presentMemory-utilized Joint hierarchical Object Learning for Navigation in IndoorRooms (MJOLNIR), a target-driven navigation algorithm, which considers theinherent relationship between target objects, and the more salient contextualobjects occurring in its surrounding. Extensive experiments conducted acrossmultiple environment settings show an $82.9\%$ and $93.5\%$ gain over existingstate-of-the-art navigation methods in terms of the success rate (SR), andsuccess weighted by path length (SPL), respectively. We also show that ourmodel learns to converge much faster than other algorithms, without sufferingfrom the well-known overfitting problem. Additional details regarding thesupplementary material and code are available athttps://sites.google.com/eng.ucsd.edu/mjolnir.

Learning hierarchical relationships for object-goal navigation