  In this paper, we propose a novel architecture and a self-supervised policy
gradient algorithm, which employs unsupervised auxiliary tasks to enable a
mobile robot to learn how to navigate to a given goal. The dependency on the
global information is eliminated by providing only sparse range-finder
measurements to the robot. The partially observable planning problem is
addressed by splitting it into a hierarchical process. We use convolutional
networks to plan locally, and a differentiable memory to provide information
about past time steps in the trajectory. These modules, combined in our network
architecture, produce globally consistent plans. The sparse reward problem is
mitigated by our modified policy gradient algorithm. We model the robots
uncertainty with unsupervised tasks to force exploration. The novel
architecture we propose with the modified version of the policy gradient
algorithm allows our robot to reach the goal in a sample efficient manner,
which is orders of magnitude faster than the current state of the art policy
gradient algorithm. Simulation and experimental results are provided to
validate the proposed approach.
