Animal Motions on Legged Robots Using Nonlinear Model Predictive Control

This work presents a motion capture-driven locomotion controller for quadrupedal robots that replicates the non-periodic footsteps and subtle body movement of animal motions. We adopt a nonlinear model predictive control (NMPC) formulation that generates optimal base trajectories and stepping locations. By optimizing both footholds and base trajectories, our controller effectively tracks retargeted animal motions with natural body movements and highly irregular strides. We demonstrate our approach with prerecorded animal motion capture data. In simulation and hardware experiments, our motion controller enables quadrupedal robots to robustly reproduce fundamental characteristics of a target animal motion regardless of the significant morphological disparity.