Soft robots are notoriously hard to control. This is partly due to the scarcity of models able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available simulation methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, a free, open-source simulation environment for soft, slender rods that can bend, twist, shear and stretch. We demonstrate how Elastica can be coupled with five state-of-the-art reinforcement learning algorithms to successfully control a soft, compliant robotic arm and complete increasingly challenging tasks.
@article{arxiv.2009.08422,
title = {Elastica: A compliant mechanics environment for soft robotic control},
author = {Noel Naughton and Jiarui Sun and Arman Tekinalp and Girish Chowdhary and Mattia Gazzola},
journal= {arXiv preprint arXiv:2009.08422},
year = {2020}
}