Simulating high-DOF human-like agents using hierarchical feedback planner.

ABSTRACTWe present a multi-agent simulation algorithm to compute the trajectories and full-body motion of human-like agents. Our formulation uses a coupled approach that combines 2D collision-free navigation with high-DOF human motion simulation using a behavioral finite state machine. In order to generate plausible pedestrian motion, we use a closed-loop hierarchical planner that satisfies dynamic stability, biomechanical, and kinematic constraints, and is tightly integrated with multi-agent navigation. Furthermore, we use motion capture data to generate natural looking human motion. The overall system is able to generate plausible motion with upper and lower body movements and avoid collisions with other human-like agents. We highlight its performance in indoor and outdoor scenarios with tens of human-like agents.