Joint stiffness tuning of exoskeleton robot H2 by tacit learning

Shingo Shimoda*, Álvaro Costa, Guillermo Asin Prieto, Shotaro Okajima, Eduardo Ináez, Yasuhisa Hasegawa, Jose M. Azor In, Jose L Pons, Juan C. Moreno

*Corresponding author for this work

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

Abstract

Joint stiffness of the exoskeleton robot is one of the most important factors to support bipedal walking. In this paper, we discuss the robot joint stiffness tuning algorithm using the bio-mimetic learning method called tacit learning. We experimentally showed that the pro- posed controller can tune the joint stiffness of the exoskeleton robot by tuning the integral gain in the controller. The walking experiment wear- ing the exoskeleton robot suggest that the stiffness tuning is applicable to control the walking speed.

Original languageEnglish (US)
Title of host publicationSymbiotic Interaction - 4th International Workshop, Symbiotic 2015, Proceedings
EditorsJonathan Freeman, Anna Spagnolli, Benjamin Blankertz, Giulio Jacucci, Luciano Gamberini, Anna Spagnolli
PublisherSpringer Verlag
Pages134-138
Number of pages5
ISBN (Print)9783319249162
DOIs
StatePublished - Jan 1 2015
Event4th International Workshop on Symbiotic Interaction, Symbiotic 2015 - Berlin, Germany
Duration: Oct 7 2015Oct 8 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9359
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Workshop on Symbiotic Interaction, Symbiotic 2015
CountryGermany
CityBerlin
Period10/7/1510/8/15

Keywords

  • Exoskeleton robot
  • Tacit learning
  • Walking support

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Joint stiffness tuning of exoskeleton robot H2 by tacit learning'. Together they form a unique fingerprint.

Cite this