  A motion-based control interface promises flexible robot operations in
dangerous environments by combining user intuitions with the robot's motor
capabilities. However, designing a motion interface for non-humanoid robots,
such as quadrupeds or hexapods, is not straightforward because different
dynamics and control strategies govern their movements. We propose a novel
motion control system that allows a human user to operate various motor tasks
seamlessly on a quadrupedal robot. We first retarget the captured human motion
into the corresponding robot motion with proper semantics using supervised
learning and post-processing techniques. Then we apply the motion imitation
learning with curriculum learning to develop a control policy that can track
the given retargeted reference. We further improve the performance of both
motion retargeting and motion imitation by training a set of experts. As we
demonstrate, a user can execute various motor tasks using our system, including
standing, sitting, tilting, manipulating, walking, and turning, on simulated
and real quadrupeds. We also conduct a set of studies to analyze the
performance gain induced by each component.
