Capability-based Frameworks for Industrial Robot Skills: a Survey

Matteo Pantano, Thomas Eiband, Dongheui Lee

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

3 Scopus citations

Abstract

The research community is puzzled with words like skill, action, atomic unit and others when describing robots' capabilities. However, for giving the possibility to integrate capabilities in industrial scenarios, a standardization of these descriptions is necessary. This work uses a structured review approach to identify commonalities and differences in the research community of robots' skill frameworks. Through this method, 210 papers were analyzed and three main results were obtained. First, the vast majority of authors agree on a taxonomy based on task, skill and primitive. Second, the most investigated robots' capabilities are pick and place. Third, industrial oriented applications focus more on simple robots' capabilities with fixed parameters while ensuring safety aspects. Therefore, this work emphasizes that a taxonomy based on task, skill and primitives should be used by future works to align with existing literature. Moreover, further research is needed in the industrial domain for parametric robots' capabilities while ensuring safety.

Original languageEnglish
Title of host publication2022 IEEE 18th International Conference on Automation Science and Engineering, CASE 2022
PublisherIEEE Computer Society
Pages2355-2362
Number of pages8
ISBN (Electronic)9781665490429
DOIs
StatePublished - 2022
Externally publishedYes
Event18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico
Duration: 20 Aug 202224 Aug 2022

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2022-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/TerritoryMexico
CityMexico City
Period20/08/2224/08/22

Keywords

  • HMLV
  • PPR
  • primitive
  • review
  • robot
  • skill
  • survey
  • task

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