  The development of algorithms for automation of subtasks during robotic
surgery can be accelerated by the availability of realistic simulation
environments. In this work, we focus on one aspect of the realism of a surgical
simulator, which is the positional accuracy of the robot. In current
simulators, robots have perfect or near-perfect accuracy, which is not
representative of their physical counterparts. We therefore propose a pair of
neural networks, trained by data collected from a physical robot, to estimate
both the controller error and the kinematic and non-kinematic error. These
error estimates are then injected within the simulator to produce a simulated
robot that has the characteristic performance of the physical robot. In this
scenario, we believe it is sufficient for the estimated error used in the
simulation to have a statistically similar distribution to the actual error of
the physical robot. This is less stringent, and therefore more tenable, than
the requirement for error compensation of a physical robot, where the estimated
error should equal the actual error. Our results demonstrate that error
injection reduces the mean position and orientation differences between the
simulated and physical robots from 5.0 mm / 3.6 deg to 1.3 mm / 1.7 deg,
respectively, which represents reductions by factors of 3.8 and 2.1.
