学习在势流中游泳
Learning to swim in potential flow
摘要：
鱼靠摆动身体来游泳。这些推进运动需要与其流体环境相互作用的身体的协调形状变化，但导致强大的转弯和游泳运动的特定形状协调仍然不清楚。为了解决水下运动规划的问题，我们提出了一个简单的模型，一个三连杆鱼游泳的势流环境中，我们使用无模型的强化学习形状控制。我们到达两个游泳任务的最佳形状变化：在所需的方向游泳和游泳对一个已知的目标。这个鱼模型属于一类几何力学问题，称为无漂移动力系统，这使我们能够分析游泳行为的几何相位在形状空间的鱼。这些几何方法在存在漂移的情况下不太直观。在这里，我们使用形状空间分析作为评估，可视化和解释通过强化学习在没有漂移的情况下获得的控制策略的工具。然后，我们研究这些政策的鲁棒性漂移相关的扰动。虽然鱼不能直接控制漂流本身，但它学会了利用适度漂流的优势到达目标。
英文摘要：
Fish swim by undulating their bodies. These propulsive motions require coordinated shape changes of a body that interacts with its fluid environment, but the specific shape coordination that leads to robust turning and swimming motions remains unclear. To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control. We arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target. This fish model belongs to a class of problems in geometric mechanics, known as driftless dynamical systems, which allow us to analyze the swimming behavior in terms of geometric phases over the shape space of the fish. These geometric methods are less intuitive in the presence of drift. Here, we use the shape space analysis as a tool for assessing, visualizing, and interpreting the control policies obtained via reinforcement learning in the absence of drift. We then examine the robustness of these policies to drift-related perturbations. Although the fish has no direct control over the drift itself, it learns to take advantage of the presence of moderate drift to reach its target.
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