Learning to Manipulate Amorphous Materials

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.