  Robots operating in human environments need various skills, like slow and
fast walking, turning, side-stepping, and many more. However, building robot
controllers that can exhibit such a large range of behaviors is a challenging
problem that requires tedious investigation for every task. We present a
unified model-based control algorithm for imitating different animal gaits
without expensive simulation training or real-world fine-tuning. Our method
consists of stance and swing leg controllers using a centroidal dynamics model
augmented with online adaptation techniques. We also develop a whole-body
trajectory optimization procedure to fix the kinematic infeasibility of the
reference animal motions. We demonstrate that our universal data-driven
model-based controller can seamlessly imitate various motor skills, including
trotting, pacing, turning, and side-stepping. It also shows better tracking
capabilities in simulation and the real world against several baselines,
including another model-based imitation controller and a learning-based motion
imitation technique.
