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.

中文翻译：

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Elastica：用于软机器人控制的合规力学环境

众所周知，软机器人难以控制。这部分是由于缺乏能够捕捉其复杂连续介质力学的模型，导致缺乏充分利用身体顺应性的控制方法。当前可用的仿真方法要么计算要求太高，要么物理假设过于简单，导致用于开发此类控制方案的可用仿真资源匮乏。为了解决这个问题，我们推出了 Elastica，这是一个免费的开源模拟环境，适用于可以弯曲、扭曲、剪切和拉伸的柔软细长杆。我们展示了 Elastica 如何与五种最先进的强化学习算法相结合，以成功控制柔软、顺从的机械臂并完成越来越具有挑战性的任务。