Incremental learning control of the DLR-HIT-hand II during interaction tasks

Alessio Alessi*, Loredana Zollo, Luca Lonini, Rosanna De Falco, Eugenio Guglielmelli

*Corresponding author for this work

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

Abstract

In this paper a bio-inspired control architecture for a robotic hand is presented. It relies on the same mechanisms of learning inverse internal models studied in humans. The control is capable of developing an internal representation of the hand interacting with the environment and updating it by means of the interaction forces that arise during contact. The learning paradigm exploits LWPR networks, which allow efficient incremental online learning through the use of spatially localized linear regression models. Additionally this paradigm limits negative interference when learning multiple tasks. The architecture is validated on a simulated finger of the DLRHIT- Hand II performing closing movements in presence of two different viscous force fields, perturbing its motion.

Original languageEnglish (US)
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages3194-3197
Number of pages4
DOIs
StatePublished - Dec 1 2010
Externally publishedYes
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: Aug 31 2010Sep 4 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Other

Other2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
CountryArgentina
CityBuenos Aires
Period8/31/109/4/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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