Elastica: A compliant mechanics environment for soft robotic control
September 17, 2020 Β· Declared Dead Β· π IEEE Robotics and Automation Letters
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Authors
Noel Naughton, Jiarui Sun, Arman Tekinalp, Girish Chowdhary, Mattia Gazzola
arXiv ID
2009.08422
Category
cs.RO: Robotics
Cross-listed
eess.SY
Citations
113
Venue
IEEE Robotics and Automation Letters
Last Checked
4 months ago
Abstract
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.
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