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PUBLICATIONS

Ontology-Based State Representations for Intention Recognition in Human-Robot Collaborative Environments

Published

Author(s)

Craig I. Schlenoff, Anthony Pietromartire, Zeid Kootbally, Stephen B. Balakirsky, 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

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Keywords

intention recognition, human-robot interaction and safety, state representation, ontology, template matching, RCC8
Manufacturing, Manufacturing systems design and analysis and Robotics

Citation

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 October 8, 2025)

Issues

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Created May 23, 2013, Updated April 7, 2017
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