We present a method of training character manipulation of amorphous materials such as those often used in cooking. Common examples of amorphous materials include granular materials (salt, uncooked rice), fluids (honey), and visco-plastic materials (sticky rice, softened butter). A typical task is to spread a given material out across a flat surface using a tool such as a scraper or knife. We use reinforcement learning to train our controllers to manipulate materials in various ways. The training is performed in a physics simulator that uses position-based dynamics of particles to simulate the materials to be manipulated. The neural network control policy is given observations of the material (e.g. a low-resolution density map), and the policy outputs actions such as rotating and translating the knife. We demonstrate policies that have been successfully trained to carry out the following tasks: spreading, gathering, and flipping. We produce a final animation by using inverse kinematics to guide a character's arm and hand to match the motion of the manipulation tool such as a knife or a frying pan.

中文翻译：

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学习操纵无定形材料

我们提出了一种训练无定形材料（例如经常用于烹饪的材料）的字符操作的方法。无定形材料的常见示例包括颗粒材料（盐、生米）、流体（蜂蜜）和粘塑性材料（糯米、软化黄油）。一个典型的任务是使用诸如刮刀或刀之类的工具将给定的材料散布在平坦的表面上。我们使用强化学习来训练我们的控制器以各种方式操纵材料。训练在物理模拟器中进行，该模拟器使用基于位置的粒子动力学来模拟要操纵的材料。神经网络控制策略被给予对材料的观察（例如低分辨率密度图），并且策略输出诸如旋转和平移刀等动作。我们展示了已成功训练以执行以下任务的策略：传播、收集和翻转。我们通过使用逆运动学来引导角色的手臂和手以匹配刀或煎锅等操作工具的运动来制作最终动画。