Examining the impact of data augmentation for psychomotor skills training in human-robot interaction

Daniel Majonica*, Y.D. Iren, R. Klemke

*Corresponding author for this work

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

Abstract

Training psychomotor skills for human-robot interaction is generally done with a human trainer educating the human on how to handle the robot effectively and interact with it safely and efficiently. The dynamic interaction between a robot and a human requires complex machine learning algorithms to be modeled, and these algorithms rely on a large amount of data to be trained. Such data are collected by sensors when a human interacts with a robot. Consequently, the data must be annotated by an expert. Finally, with the annotated data, a psychomotor skills training model can be created to assist the training process. This is a time intensive and costly process. To ease the costs and cut down collection time, we propose the use of data augmentation.
Original languageEnglish
Title of host publicationProceedings of the Doctoral Consortium of Sixteenth European Conference on Technology Enhanced Learning
Subtitle of host publicationVirtual Event, Bolzano, Italy, September 20–21, 2021
EditorsMikhail Fominykh, Maria Aristeidou
PublisherCEUR-WS.org
Pages83-88
Number of pages6
DOIs
Publication statusPublished - 18 Jan 2021
EventDoctoral Consortium at the European Conference on Technology Enhanced Learning 2021 - Bolzano, Italy
Duration: 20 Sep 202121 Sep 2021
http://ceur-ws.org/Vol-3076/

Conference

ConferenceDoctoral Consortium at the European Conference on Technology Enhanced Learning 2021
Abbreviated titleDCECTEL 2021
Country/TerritoryItaly
CityBolzano
Period20/09/2121/09/21
Internet address

Keywords

  • Human-robot interaction
  • data augmentation
  • machine learning

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