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Publication Citation: Inferring Intention Through State Representations in Cooperative Human-Robot Environments

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Author(s): Craig I. Schlenoff; Anthony Pietromartire; Zeid Kootbally; Stephen B. Balakirsky; Thomas R. Kramer; Sebti Foufou;
Title: Inferring Intention Through State Representations in Cooperative Human-Robot Environments
Published: June 07, 2013
Abstract: In this paper, we describe a novel approach for inferring intention during cooperative human-robot activities through the representation and ordering of state information. State relationships are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. The combination of all relevant state relationships at a given point in time constitutes a state. A template matching approach is used to match state relations to known intentions. This approach is applied to a manufacturing kitting operation, where humans and robots are working together to develop kits. Based upon the sequences of a set of predefined high-level state 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.
Citation: Engineering Creative Design in Robotics and Mechatronics
Publisher: IGI Global, Hershey, PA
Pages: 34 pp.
Keywords: intention recognition; human-robot interaction and safety; state representation; ontology; tenplate matching
Research Areas: Performance Metrics, Robotics, Ontologies, Process Improvement, Manufacturing
PDF version: PDF Document Click here to retrieve PDF version of paper (2MB)