Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem. A number of solutions have been proposed in recent years, but we have found that many works do not present a thorough evaluation in the real world, or underplay the significant engineering effort and task-specific fine tuning that is required to achieve the published results. In this paper, we dive deeper into the sim-to-real transfer challenge, investigate why this is such a difficult problem, and present objective evaluations of a number of transfer methods across a range of real-world tasks. Surprisingly, we found that a method which simply injects random forces into the simulation performs just as well as more complex methods, such as those which randomise the simulator's dynamics parameters, or adapt a policy online using recurrent network architectures.
@article{arxiv.2008.06686,
title = {Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics},
author = {Eugene Valassakis and Zihan Ding and Edward Johns},
journal= {arXiv preprint arXiv:2008.06686},
year = {2020}
}
Comments
To be published at IROS 2020. 8 pages, 6 figures. For supplementary material and code, please visit : https://www.robot-learning.uk/crossing-the-gap