  We propose a novel approach to the 'reality gap' problem, i.e., modifying a
robot simulation so that its performance becomes more similar to observed real
world phenomena. This problem arises whether the simulation is being used by
human designers or in an automated policy development mechanism. We expect that
the program/policy is developed using simulation, and subsequently deployed on
a real system. We further assume that the program includes a monitor procedure
with scalar output to determine when it is achieving its performance
objectives. The proposed approach collects simulation and real world
observations and builds conditional probability functions. These are used to
generate paired roll-outs to identify points of divergence in behavior. These
are used to generate {\it state-space kernels} that coerce the simulation into
behaving more like observed reality.
  The method was evaluated using ROS/Gazebo for simulation and a heavily
modified Traaxas platform in outdoor deployment. The results support not just
that the kernel approach can force the simulation to behave more like reality,
but that the modification is such that an improved control policy tested in the
modified simulation also performs better in the real world.
