Online Virtual Repellent Point Adaptation for Biped Walking using Iterative Learning Control

Shengzhi Wang, George Mesesan, Johannes Englsberger, Dongheui Lee, Christian Ott

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We propose an online learning framework to reduce the effect of model inaccuracies and improve the robustness of the Divergent Component of Motion (DCM)-based walking algorithm. This framework uses the iterative learning control (ILC) theory for learning an adjusted Virtual Repellent Point (VRP) reference trajectory based on the current VRP error. The learned VRP reference waypoints are saved in a memory buffer and used in the subsequent walking iteration. Based on the availability of force-torque (FT) sensors, we propose two different implementations using different VRP error signals for learning: measurement-error-based and commanded-errorbased framework. Both implementations reduce the average VRP errors and demonstrate improved walking robustness. The measurement-error-based framework has better reference trajectory tracking performance for the measured VRP.

Original languageEnglish
Title of host publication2020 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
EditorsTamim Asfour, Dongheui Lee, Mombaur Katja, Katsu Yamane, Kensuke Harada, Ludovic Righetti, Nikos Tsagarakis, Tomomichi Sugihara
PublisherIEEE Computer Society
Pages112-119
Number of pages8
ISBN (Electronic)9781728193724
DOIs
StatePublished - 2021
Externally publishedYes
Event20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020 - Munich, Germany
Duration: 19 Jul 202121 Jul 2021

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2021-July
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
Country/TerritoryGermany
CityMunich
Period19/07/2121/07/21

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