Towards a Natural Motion Generator: a Pipeline to Control a Humanoid based on Motion Data
Abstract
Imitation of the upper body motions of human demonstrators or animation characters to human-shaped robots is studied in this paper. We present a pipeline for motion retargeting by transferring the joints of interest (JOI) of source motions to the target humanoid robot. To this end, we deploy an optimization-based motion retargeting method utilizing link length modifications of the source skeleton and a task (Cartesian) space fine-tuning of JOI motion descriptors. To evaluate the effectiveness of the proposed pipeline, we use two different 3-D motion datasets from three human demonstrators and an Ogre animation character, Bork, and successfully transfer the motions to four different humanoid robots: DARwIn-OP, COmpliant HuMANoid Platform (COMAN), THORMANG, and Atlas. Furthermore, COMAN and THORMANG are actually controlled to show that the proposed method can be deployed to physical robots.
BibTeX
@inproceedings{iros2019_towardsanaturalm,
title = {Towards a Natural Motion Generator: a Pipeline to Control a Humanoid based on Motion Data},
author = {Sungjoon Choi and Joohyung Kim},
booktitle = {IROS 2019},
year = {2019}
}