Ontology-based state representation for intention recognition in cooperative human-robot environments

Craig Schlenoff, Anthony Pietromartire, Zeid Kootbally, Stephen Balakirsky, Sebti Foufou

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

Abstract

In this paper, we describe a novel approach for representing state information for the purpose of intention recognition in cooperative human-robot environments. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the detailed state information presented in this paper to infer the probability of subsequent actions occurring. This would enable the robot to better help the human with the operation or, at a minimum, better stay out of his or her way.

Original languageEnglish (US)
Title of host publicationUbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing
PublisherAssociation for Computing Machinery
Pages810-817
Number of pages8
ISBN (Print)9781450312240
DOIs
StatePublished - 2012
Event14th International Conference on Ubiquitous Computing, UbiComp 2012 - Pittsburgh, PA, United States
Duration: Sep 5 2012Sep 8 2012

Publication series

NameUbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Other

Other14th International Conference on Ubiquitous Computing, UbiComp 2012
Country/TerritoryUnited States
CityPittsburgh, PA
Period9/5/129/8/12

Keywords

  • Human robot safety
  • Intention recognition
  • Ontologies
  • RCC8
  • Robotics
  • SWRL
  • State-based representation

ASJC Scopus subject areas

  • Software

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