Learning to Manipulate Amorphous Materials
This work addresses the challenge of simulating realistic material interactions for applications like cooking or robotics, but it is incremental as it builds on existing reinforcement learning and physics simulation methods.
The paper tackles the problem of training character controllers to manipulate amorphous materials like granular substances and fluids using reinforcement learning, achieving successful policies for tasks such as spreading, gathering, and flipping in a physics simulator.
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.