  Classical autonomous navigation systems can control robots in a
collision-free manner, oftentimes with verifiable safety and explainability.
When facing new environments, however, fine-tuning of the system parameters by
an expert is typically required before the system can navigate as expected. To
alleviate this requirement, the recently-proposed Adaptive Planner Parameter
Learning paradigm allows robots to \emph{learn} how to dynamically adjust
planner parameters using a teleoperated demonstration or corrective
interventions from non-expert users. However, these interaction modalities
require users to take full control of the moving robot, which requires the
users to be familiar with robot teleoperation. As an alternative, we introduce
\textsc{apple}, Adaptive Planner Parameter Learning from \emph{Evaluative
Feedback} (real-time, scalar-valued assessments of behavior), which represents
a less-demanding modality of interaction. Simulated and physical experiments
show \textsc{apple} can achieve better performance compared to the planner with
static default parameters and even yield improvement over learned parameters
from richer interaction modalities.
