Recent advancements in external memory based neural networks have shown promise

in solving tasks that require precise storage and retrieval of past information. Re-

searchers have applied these models to a wide range of tasks that have algorithmic

properties but have not applied these models to real-world robotic tasks. In this

thesis, we present memory-augmented neural networks that synthesize robot navigation policies which a) encode long-term temporal dependencies b) make decisions in

partially observed environments and c) quantify the uncertainty inherent in the task.

We extract information about the temporal structure of a task via imitation learning

from human demonstration and evaluate the performance of the models on control

policies for a robot navigation task. Experiments are performed in partially observed

environments in both simulation and the real world