Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).
NIST Authors in Bold
|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|
|Keywords:||intention recognition, human-robot interaction and safety, state representation, ontology, tenplate matching|
|Research Areas:||Performance Metrics, Robotics, Ontologies, Process Improvement, Manufacturing|
|PDF version:||Click here to retrieve PDF version of paper (2MB)|