  Human motion driven control (HMDC) is an effective approach for generating
natural and compelling robot motions while preserving high-level semantics.
However, establishing the correspondence between humans and robots with
different body structures is not straightforward due to the mismatches in
kinematics and dynamics properties, which causes intrinsic ambiguity to the
problem. Many previous algorithms approach this motion retargeting problem with
unsupervised learning, which requires the prerequisite skill sets. However, it
will be extremely costly to learn all the skills without understanding the
given human motions, particularly for high-dimensional robots. In this work, we
introduce CrossLoco, a guided unsupervised reinforcement learning framework
that simultaneously learns robot skills and their correspondence to human
motions. Our key innovation is to introduce a cycle-consistency-based reward
term designed to maximize the mutual information between human motions and
robot states. We demonstrate that the proposed framework can generate
compelling robot motions by translating diverse human motions, such as running,
hopping, and dancing. We quantitatively compare our CrossLoco against the
manually engineered and unsupervised baseline algorithms along with the ablated
versions of our framework and demonstrate that our method translates human
motions with better accuracy, diversity, and user preference. We also showcase
its utility in other applications, such as synthesizing robot movements from
language input and enabling interactive robot control.
