Real-Time Planning for Parameterized Human Motion

Abstract

We present a novel approach to learn motion controllers for real-time character animation based on motion capture data. We employ a tree-based regression algorithm for reinforcement learning, which enables us to generate motions that require planning. This approach is more flexible and more robust than previous strategies. We also extend the learning framework to include parameterized motions and interpolation. This enables us to control the character more precisely with a small amount of motion data. Finally, we present results of our algorithm for three different types of controllers.