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

TuneNet: One-Shot Residual Tuning for System Identification and Sim-to-Real Robot Task Transfer