A Machine Learning Approach to Minimization of the Sim-To-Real Gap via Precise Dynamics Modeling of a Fast Moving Robot

How well simulation results can be transferred to the real world depends to a large extent on the sim-to-real gap that therefore should be as small as possible. In this work, this gap is reduced exemplarily for a robot with an omni-directional drive, which is challenging to simulate, utilizing machine learning methods. For this purpose, a motion capture system is first used to record a suitable data set of the robot’s movements. Then, a model based on physical principles and observations is designed manually, which includes some unknown parameters that are learned based on the training dataset. Since the model is not differentiable, the evolutionary algorithms NSGA-II and -III are applied. Finally, by the presented approach, a significant reduction of the sim-to-real gap can be observed even at higher velocities above 2 m/s.