  Direct search for objects as part of navigation poses a challenge for small
items. Utilizing context in the form of object-object relationships enable
hierarchical search for targets efficiently. Most of the current approaches
tend to directly incorporate sensory input into a reward-based learning
approach, without learning about object relationships in the natural
environment, and thus generalize poorly across domains. We present
Memory-utilized Joint hierarchical Object Learning for Navigation in Indoor
Rooms (MJOLNIR), a target-driven navigation algorithm, which considers the
inherent relationship between target objects, and the more salient contextual
objects occurring in its surrounding. Extensive experiments conducted across
multiple environment settings show an $82.9\%$ and $93.5\%$ gain over existing
state-of-the-art navigation methods in terms of the success rate (SR), and
success weighted by path length (SPL), respectively. We also show that our
model learns to converge much faster than other algorithms, without suffering
from the well-known overfitting problem. Additional details regarding the
supplementary material and code are available at
https://sites.google.com/eng.ucsd.edu/mjolnir.
