  Motion retargeting from human to robot remains a very challenging task due to
variations in the structure of humans and robots. Most traditional
optimization-based algorithms solve this problem by minimizing an objective
function, which is usually time-consuming and heavily dependent on good
initialization. In contrast, methods with feedforward neural networks can learn
prior knowledge from training data and quickly infer the results, but these
methods also suffer from the generalization problem on unseen actions, leading
to some infeasible results. In this paper, we propose a novel neural
optimization approach taking advantages of both kinds of methods. A graph-based
neural network is utilized to establish a mapping between the latent space and
the robot motion space. Afterward, the retargeting results can be obtained by
searching for the optimal vector in this latent space. In addition, a deep
encoder also provides a promising initialization for better and faster
convergence. We perform experiments on retargeting Chinese sign language to
three different kinds of robots in the simulation environment, including ABB's
YuMi dual-arm collaborative robot, NAO and Pepper. A real-world experiment is
also conducted on the Yumi robot. Experimental results show that our method can
retarget motion from human to robot with both efficiency and accuracy.
