Synthesizing Human-Like Walking in Constrained Environments

Abstract

We present a new algorithm to generate plausible walking motion for high-DOF human-like articulated figures in constrained environments with multiple obstacles. Our approach combines hierarchical model decomposition with sample-based planning to efficiently compute a collision-free path in tight spaces. Furthermore, we use path perturbation and replanning techniques to satisfy the kinematic and dynamic constraints on the motion. In order to generate realistic human-like motion, we present a new motion blending algorithm that refines the path computed by the planner with motion capture data to compute a smooth and plausible trajectory. We demonstrate the results of generating motion corresponding to placing or lifting object, walking and bending for a 34-DOF articulated model.