Schlenoff, C.
, Pietromartire, A.
, Kootbally, Z.
, Balakirsky, S.
and Foufou, S.
(2013),
Ontology-Based State Representations for Intention Recognition in Human-Robot Collaborative Environments, Robotics and Autonomous Systems Journal, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=913796
(Accessed November 21, 2024)
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Ontology-Based State Representations for Intention Recognition in Human-Robot Collaborative Environments
Published
Author(s)
, , , , Sebti Foufou
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 described in this paper to infer the probability of subsequent actions occurring. This would allow the robot to better help the human with the task or, at a minimum, better stay out of his or her way.Citation
Robotics and Autonomous Systems Journal
Issue
61
Pub Type
Journals
Download Paper
Keywords
intention recognition, human-robot interaction and safety, state representation, ontology, template matching, RCC8
Citation
Issues
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Created May 23, 2013, Updated April 7, 2017