Crossing the Reality Gap: a Short Introduction to the Transferability Approach
This addresses the reality gap in robotics for researchers using simulation-based optimization, though it is incremental as it builds on existing transferability concepts.
The paper tackles the problem of solutions optimized in simulation failing on real robots by introducing a transferability function that quantifies simulation-reality mismatch, and demonstrates its learning through regression and experiments on a quadruped robot.
In robotics, gradient-free optimization algorithms (e.g. evolutionary algorithms) are often used only in simulation because they require the evaluation of many candidate solutions. Nevertheless, solutions obtained in simulation often do not work well on the real device. The transferability approach aims at crossing this gap between simulation and reality by \emph{making the optimization algorithm aware of the limits of the simulation}. In the present paper, we first describe the transferability function, that maps solution descriptors to a score representing how well a simulator matches the reality. We then show that this function can be learned using a regression algorithm and a few experiments with the real devices. Our results are supported by an extensive study of the reality gap for a simple quadruped robot whose control parameters are optimized. In particular, we mapped the whole search space in reality and in simulation to understand the differences between the fitness landscapes.