  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.
