Elastica: A compliant mechanics environment for soft robotic control
This addresses the scarcity of accurate and efficient simulation tools for soft robotic control, enabling better development of control methodologies for researchers and engineers in robotics.
The paper tackles the problem of controlling soft robots by introducing Elastica, a free, open-source simulation environment for soft, slender rods that can bend, twist, shear, and stretch, and demonstrates its coupling with five state-of-the-art reinforcement learning algorithms to successfully control a soft robotic arm in increasingly challenging tasks.
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.