  We propose a light-weight, self-supervised adaptation for a visual navigation
agent to generalize to unseen environment. Given an embodied agent trained in a
noiseless environment, our objective is to transfer the agent to a noisy
environment where actuation and odometry sensor noise is present. Our method
encourages the agent to maximize the consistency between the global maps
generated at different time steps in a round-trip trajectory. The proposed task
is completely self-supervised, not requiring any supervision from ground-truth
pose data or explicit noise model. In addition, optimization of the task
objective is extremely light-weight, as training terminates within a few
minutes on a commodity GPU. Our experiments show that the proposed task helps
the agent to successfully transfer to new, noisy environments. The transferred
agent exhibits improved localization and mapping accuracy, further leading to
enhanced performance in downstream visual navigation tasks. Moreover, we
demonstrate test-time adaptation with our self-supervised task to show its
potential applicability in real-world deployment.
