  We focus on the utilisation of reactive trajectory imitation controllers for
goal-directed mobile robot navigation. We propose a topological navigation
graph (TNG) - an imitation-learning-based framework for navigating through
environments with intersecting trajectories. The TNG framework represents the
environment as a directed graph composed of deep neural networks. Each vertex
of the graph corresponds to a trajectory and is represented by a trajectory
identification classifier and a trajectory imitation controller. For trajectory
following, we propose the novel use of neural object detection architectures.
The edges of TNG correspond to intersections between trajectories and are all
represented by a classifier. We provide empirical evaluation of the proposed
navigation framework and its components in simulated and real-world
environments, demonstrating that TNG allows us to utilise non-goal-directed,
imitation-learning methods for goal-directed autonomous navigation.
