  Motion retargeting from a human demonstration to a robot is an effective way
to reduce the professional requirements and workload of robot programming, but
faces the challenges resulting from the differences between humans and robots.
Traditional optimization-based methods are time-consuming and rely heavily on
good initialization, while recent studies using feedforward neural networks
suffer from poor generalization to unseen motions. Moreover, they neglect the
topological information in human skeletons and robot structures. In this paper,
we propose a novel neural latent optimization approach to address these
problems. Latent optimization utilizes a decoder to establish a mapping between
the latent space and the robot motion space. Afterward, the retargeting results
that satisfy robot constraints can be obtained by searching for the optimal
latent vector. Alongside with latent optimization, neural initialization
exploits an encoder to provide a better initialization for faster and better
convergence of optimization. Both the human skeleton and the robot structure
are modeled as graphs to make better use of topological information. We perform
experiments on retargeting Chinese sign language, which involves two arms and
two hands, with additional requirements on the relative relationships among
joints. Experiments include retargeting various human demonstrations to YuMi,
NAO, and Pepper in the simulation environment and to YuMi in the real-world
environment. Both efficiency and accuracy of the proposed method are verified.
