  While classical autonomous navigation systems can typically move robots from
one point to another safely and in a collision-free manner, these systems may
fail or produce suboptimal behavior in certain scenarios. The current practice
in such scenarios is to manually re-tune the system's parameters, e.g. max
speed, sampling rate, inflation radius, to optimize performance. This practice
requires expert knowledge and may jeopardize performance in the originally good
scenarios. Meanwhile, it is relatively easy for a human to identify those
failure or suboptimal cases and provide a teleoperated intervention to correct
the failure or suboptimal behavior. In this work, we seek to learn from those
human interventions to improve navigation performance. In particular, we
propose Adaptive Planner Parameter Learning from Interventions (APPLI), in
which multiple sets of navigation parameters are learned during training and
applied based on a confidence measure to the underlying navigation system
during deployment. In our physical experiments, the robot achieves better
performance compared to the planner with static default parameters, and even
dynamic parameters learned from a full human demonstration. We also show
APPLI's generalizability in another unseen physical test course, and a suite of
300 simulated navigation environments.
