  Robots need to be able to work in multiple different environments. Even when
performing similar tasks, different behaviour should be deployed to best fit
the current environment. In this paper, We propose a new approach to
navigation, where it is treated as a multi-task learning problem. This enables
the robot to learn to behave differently in visual navigation tasks for
different environments while also learning shared expertise across
environments. We evaluated our approach in both simulated environments as well
as real-world data. Our method allows our system to converge with a 26%
reduction in training time, while also increasing accuracy.
