Online Learning of Centroidal Angular Momentum towards Enhancing DCM-based Locomotion
Robert Schuller, George Mesesan, Johannes Englsberger, Jinoh Lee, Christian Ott
Abstract
Gait generation frameworks for humanoid robots typically assume a constant centroidal angular momentum (CAM) throughout the walking cycle, which induces undesirable contact torques in the feet and results in performance degradation. In this work, we present a novel algorithm to learn the CAM online and include the obtained knowledge within the closed-form solutions of the Divergent Component of Motion (DCM) locomotion framework. To ensure a reduction of the contact torques at the desired center of pressure position, a CAM trajectory is generated and explicitly tracked by a whole-body controller. Experiments with the humanoid robot TORO demonstrate that the proposed method significantly increases the maximum step length and walking speed during locomotion.
BibTeX
@inproceedings{icra2022_onlinelearningof,
title = {Online Learning of Centroidal Angular Momentum towards Enhancing DCM-based Locomotion},
author = {Robert Schuller and George Mesesan and Johannes Englsberger and Jinoh Lee and Christian Ott},
booktitle = {ICRA 2022},
year = {2022}
}