Robotics: Science and Systems XVI

Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space

Sungjoon Choi, Matthew Pan, Joohyung Kim

Abstract:

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.

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Bibtex:

  
@INPROCEEDINGS{Choi-RSS-20, 
    AUTHOR    = {Sungjoon Choi AND Matthew Pan AND Joohyung Kim}, 
    TITLE     = {{Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2020}, 
    ADDRESS   = {Corvalis, Oregon, USA}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2020.XVI.071} 
}