  While current autonomous navigation systems allow robots to successfully
drive themselves from one point to another in specific environments, they
typically require extensive manual parameter re-tuning by human robotics
experts in order to function in new environments. Furthermore, even for just
one complex environment, a single set of fine-tuned parameters may not work
well in different regions of that environment. These problems prohibit reliable
mobile robot deployment by non-expert users. As a remedy, we propose Adaptive
Planner Parameter Learning (APPL), a machine learning framework that can
leverage non-expert human interaction via several modalities -- including
teleoperated demonstrations, corrective interventions, and evaluative feedback
-- and also unsupervised reinforcement learning to learn a parameter policy
that can dynamically adjust the parameters of classical navigation systems in
response to changes in the environment. APPL inherits safety and explainability
from classical navigation systems while also enjoying the benefits of machine
learning, i.e., the ability to adapt and improve from experience. We present a
suite of individual APPL methods and also a unifying cycle-of-learning scheme
that combines all the proposed methods in a framework that can improve
navigation performance through continual, iterative human interaction and
simulation training.
