Nonparametric Motion Retargeting for Humanoid Robots on Shared Latent Space

Sungjoon Choi, Matt Pan, Joohyung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationRobotics
Subtitle of host publicationScience and Systems XVI
EditorsMarc Toussaint, Antonio Bicchi, Tucker Hermans
PublisherMIT Press Journals
ISBN (Print)9780992374761
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event16th Robotics: Science and Systems, RSS 2020 - Virtual, Online
Duration: 2020 Jul 122020 Jul 16

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X

Conference

Conference16th Robotics: Science and Systems, RSS 2020
CityVirtual, Online
Period20/7/1220/7/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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