  The large demand for simulated data has made the reality gap a problem on the
forefront of robotics. We propose a method to traverse the gap by tuning
available simulation parameters. Through the optimisation of physics engine
parameters, we show that we are able to narrow the gap between simulated
solutions and a real world dataset, and thus allow more ready transfer of
leaned behaviours between the two. We subsequently gain understanding as to the
importance of specific simulator parameters, which is of broad interest to the
robotic machine learning community. We find that even optimised for different
tasks that different physics engine perform better in certain scenarios and
that friction and maximum actuator velocity are tightly bounded parameters that
greatly impact the transference of simulated solutions.
