论文标题
Elastica：适合软机器人控制的符合机械环境
Elastica: A compliant mechanics environment for soft robotic control
论文作者
论文摘要
众所周知，软机器人很难控制。这部分是由于模型缺乏能够捕获其复杂的连续性力学的稀缺性，导致缺乏控制人体依从性的控制方法。当前可用的仿真方法在其物理假设上要么过于计算要求，要么过于简单，从而导致无法开发此类控制方案的可用仿真资源。为了解决这个问题，我们介绍了Elastica，这是一个免费的，开源的模拟环境，适用于柔软的细长杆，可以弯曲，扭曲，剪切和拉伸。我们展示了如何将弹性与五种最先进的强化学习算法结合在一起，以成功控制一个柔软的，兼容的机器人手臂和越来越具有挑战性的任务。
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.
