  As researchers teach robots to perform more and more complex tasks, the need
for realistic simulation environments is growing. Existing techniques for
closing the reality gap by approximating real-world physics often require
extensive real world data and/or thousands of simulation samples. This paper
presents TuneNet, a new machine learning-based method to directly tune the
parameters of one model to match another using an *iterative residual tuning*
technique. TuneNet estimates the parameter difference between two models using
a single observation from the target and minimal simulation, allowing rapid,
accurate and sample-efficient parameter estimation. The system can be trained
via supervised learning over an auto-generated simulated dataset. We show that
TuneNet can perform system identification, even when the true parameter values
lie well outside the distribution seen during training, and demonstrate that
simulators tuned with TuneNet outperform existing techniques for predicting
rigid body motion. Finally, we show that our method can estimate real-world
parameter values, allowing a robot to perform sim-to-real task transfer on a
dynamic manipulation task unseen during training. Code and videos are available
online at http://bit.ly/2lf1bAw.
