@inproceedings{e54f21aadfdd496a93ee513577df3607,
title = "Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space",
abstract = "In this work, we present a semi-supervised learning method to transfer human motion data to humanoid robots with its emphasis on the feasibility of transferred robot motions. To this end, we propose a data-driven motion retargeting method named locally weighted latent learning (LWL2 ) 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, the online puppeteering of a real humanoid robot is implemented.",
author = "Sungjoon Choi and Matt Pan and Joohyung Kim",
note = "Publisher Copyright: {\textcopyright} 2020, MIT Press Journals. All rights reserved.; 16th Robotics: Science and Systems, RSS 2020 ; Conference date: 12-07-2020 Through 16-07-2020",
year = "2020",
doi = "10.15607/RSS.2020.XVI.071",
language = "English (US)",
isbn = "9780992374761",
series = "Robotics: Science and Systems",
publisher = "MIT Press Journals",
editor = "Marc Toussaint and Antonio Bicchi and Tucker Hermans",
booktitle = "Robotics",
address = "United States",
}