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
this https URL

该研究提出了一种基于目标导向的导航算法 MJOLNIR，利用对象之间的关系和环境 context 来进行目标定位，相较于现有方法，在多种环境下实现了 82.9% 和 93.5% 的更高成功率和更短路径长度，并且收敛速度更快，并避免了过拟合问题。