  This work introduces a motion retargeting approach for legged robots, which
aims to create motion controllers that imitate the fine behavior of animals.
Our approach, namely spatio-temporal motion retargeting (STMR), guides
imitation learning procedures by transferring motion from source to target,
effectively bridging the morphological disparities by ensuring the feasibility
of imitation on the target system. Our STMR method comprises two components:
spatial motion retargeting (SMR) and temporal motion retargeting (TMR). On the
one hand, SMR tackles motion retargeting at the kinematic level by generating
kinematically feasible whole-body motions from keypoint trajectories. On the
other hand, TMR aims to retarget motion at the dynamic level by optimizing
motion in the temporal domain. We showcase the effectiveness of our method in
facilitating Imitation Learning (IL) for complex animal movements through a
series of simulation and hardware experiments. In these experiments, our STMR
method successfully tailored complex animal motions from various media,
including video captured by a hand-held camera, to fit the morphology and
physical properties of the target robots. This enabled RL policy training for
precise motion tracking, while baseline methods struggled with highly dynamic
motion involving flying phases. Moreover, we validated that the control policy
can successfully imitate six different motions in two quadruped robots with
different dimensions and physical properties in real-world settings.
