Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space

In this work, we present a semi-supervised learning method to transfer human motion data to humanoid robots with varying kinematic configurations while avoiding self-collisions.To this end, we propose a data-driven motion retargeting named locally weighted latent learning which possesses the benefits of both nonparametric regression and deep latent variable modeling.The method can leverage both paired and domain-specific datasets and can maintain robot motion feasibility owing to the nonparametric regression and graph-based heuristics it uses. The proposed method is evaluated using two different humanoid robots,the Robotis ThorMang and COMAN, in simulation environments with diverse motion capture datasets. Furthermore, online puppeteering of a real humanoid robot is implemented.