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

Authors:
Craig Schlenoff;Anthony Pietromartire;Zeid Kootbally;Stephen Balakirsky;Sebti Foufou
Affiliations:
National Institute of Standards and Technology (NIST), Gaithersburg MD and University of Burgundy, Dijon, France;National Institute of Standards and Technology (NIST), Gaithersburg MD and University of Burgundy, Dijon, France;National Institute of Standards and Technology (NIST), Gaithersburg MD;National Institute of Standards and Technology (NIST), Gaithersburg MD;University of Burgundy, Dijon, France and Qatar University, Doha Qatar
Venue:
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Year:
2012

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Ontology-based state representations for intention recognition in human-robot collaborative environments

Robotics and Autonomous Systems

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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.