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

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

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

4 Zitate (Scopus)

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.

OriginalspracheEnglisch
Titel2020 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
Redakteure/-innenTamim Asfour, Dongheui Lee, Mombaur Katja, Katsu Yamane, Kensuke Harada, Ludovic Righetti, Nikos Tsagarakis, Tomomichi Sugihara
Herausgeber (Verlag)IEEE Computer Society
Seiten112-119
Seitenumfang8
ISBN (elektronisch)9781728193724
DOIs
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
Veranstaltung20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020 - Munich, Deutschland
Dauer: 19 Juli 202121 Juli 2021

Publikationsreihe

NameIEEE-RAS International Conference on Humanoid Robots
Band2021-July
ISSN (Print)2164-0572
ISSN (elektronisch)2164-0580

Konferenz

Konferenz20th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2020
Land/GebietDeutschland
OrtMunich
Zeitraum19/07/2121/07/21

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