  Existing autonomous robot navigation systems allow robots to move from one
point to another in a collision-free manner. However, when facing new
environments, these systems generally require re-tuning by expert roboticists
with a good understanding of the inner workings of the navigation system. In
contrast, even users who are unversed in the details of robot navigation
algorithms can generate desirable navigation behavior in new environments via
teleoperation. In this paper, we introduce APPLD, Adaptive Planner Parameter
Learning from Demonstration, that allows existing navigation systems to be
successfully applied to new complex environments, given only a human
teleoperated demonstration of desirable navigation. APPLD is verified on two
robots running different navigation systems in different environments.
Experimental results show that APPLD can outperform navigation systems with the
default and expert-tuned parameters, and even the human demonstrator
themselves.
