Underwater soft robots are challenging to model and control because of their high degrees of freedom and their intricate coupling with water. In this letter, we present a method that leverages the recent development in differentiable simulation coupled with a differentiable, analytical hydrodynamic model to assist with the modeling and control of an underwater soft robot. We apply this method to Starfish, a customized soft robot design that is easy to fabricate and intuitive to manipulate. Our method starts with data obtained from the real robot and alternates between simulation and experiments. Specifically, the simulation step uses gradients from a differentiable simulator to run system identification and trajectory optimization, and the experiment step executes the optimized trajectory on the robot to collect new data to be fed into simulation. Our demonstration on Starfish shows that proper usage of gradients from a differentiable simulator not only narrows down its simulation-to-reality gap but also improves the performance of an open-loop controller in real experiments.

中文翻译：

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水下微机器人的建模与控制及微分仿真

水下软机器人具有很高的自由度以及与水的复杂结合，因此在建模和控制方面存在挑战。在这封信中，我们提出了一种方法，该方法利用了可微分模拟的最新发展以及可微分的解析流体力学模型，以辅助水下软机器人的建模和控制。我们将这种方法应用于Starfish，这是一种易于制造且易于操作的定制软机器人设计。我们的方法从真实机器人获得的数据开始，然后在模拟和实验之间交替进行。具体而言，仿真步骤使用来自可区分仿真器的梯度来运行系统识别和轨迹优化，而实验步骤在机器人上执行优化轨迹以收集要馈送到仿真中的新数据。