  In this work we focus on improving the efficiency and generalisation of
learned navigation strategies when transferred from its training environment to
previously unseen ones. We present an extension of the residual reinforcement
learning framework from the robotic manipulation literature and adapt it to the
vast and unstructured environments that mobile robots can operate in. The
concept is based on learning a residual control effect to add to a typical
sub-optimal classical controller in order to close the performance gap, whilst
guiding the exploration process during training for improved data efficiency.
We exploit this tight coupling and propose a novel deployment strategy,
switching Residual Reactive Navigation (sRRN), which yields efficient
trajectories whilst probabilistically switching to a classical controller in
cases of high policy uncertainty. Our approach achieves improved performance
over end-to-end alternatives and can be incorporated as part of a complete
navigation stack for cluttered indoor navigation tasks in the real world. The
code and training environment for this project is made publicly available at
https://sites.google.com/view/srrn/home.
