  The advent of deep learning has inspired research into end-to-end learning
for a variety of problem domains in robotics. For navigation, the resulting
methods may not have the generalization properties desired let alone match the
performance of traditional methods. Instead of learning a navigation policy, we
explore learning an adaptive policy in the parameter space of an existing
navigation module. Having adaptive parameters provides the navigation module
with a family of policies that can be dynamically reconfigured based on the
local scene structure, and addresses the common assertion in machine learning
that engineered solutions are inflexible. Of the methods tested, reinforcement
learning (RL) is shown to provide a significant performance boost to a modern
navigation method through reduced sensitivity of its success rate to
environmental clutter. The outcomes indicate that RL as a meta-policy learner,
or dynamic parameter tuner, effectively robustifies algorithms sensitive to
external, measurable nuisance factors.
