  Visual perception and navigation have emerged as major focus areas in the
field of embodied artificial intelligence. We consider the task of image-goal
navigation, where an agent is tasked to navigate to a goal specified by an
image, relying only on images from an onboard camera. This task is particularly
challenging since it demands robust scene understanding, goal-oriented planning
and long-horizon navigation. Most existing approaches typically learn
navigation policies reliant on recurrent neural networks trained via online
reinforcement learning. However, training such policies requires substantial
computational resources and time, and performance of these models is not
reliable on long-horizon navigation. In this work, we present a generative
Transformer based model that jointly models image goals, camera observations
and the robot's past actions to predict future actions. We use state-of-the-art
perception models and navigation policies to learn robust goal conditioned
policies without the need for real-time interaction with the environment. Our
model demonstrates capability in capturing and associating visual information
across long time horizons, helping in effective navigation.
  NOTE: This work was submitted as part of a Master's Capstone Project and must
be treated as such. This is still an early work in progress and not the final
version.
