@inproceedings{97092bf9aa02415c863e2a497b8def66,
title = "Evaluating state-based intention recognition algorithms against human performance",
abstract = "In this paper, we describe a novel intention recognition approach based on the representation of state information in a cooperative human-robot environment. We compare the output of the intention recognition algorithms to those of an experiment involving humans attempting to recognize the same intentions in a manufacturing kitting domain. States are represented by a combination of spatial relationships in a Cartesian frame along with cardinal direction information. Based upon a set of predefined high-level states relationships that must be true for future actions to occur, a robot can use the approaches described in this paper to infer the likelihood 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.",
keywords = "Human performance, Human robot safety, Intention recognition, Ontologies, Rcc-8, Robotics, State-based representation",
author = "Craig Schlenoff and Sebti Foufou",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 2nd International Conference on Robot Intelligence Technology and Applications, RiTA 2013 ; Conference date: 18-12-2013 Through 20-12-2013",
year = "2014",
doi = "10.1007/978-3-319-05582-4_19",
language = "English (US)",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "219--232",
editor = "Fakhri Karray and Matson, {Eric T.} and Hyun Myung and Jong-Hwan Kim and Matson, {Eric T.} and Peter Xu",
booktitle = "Robot Intelligence Technology and Applications 2 - Results from the 2nd International Conference on Robot Intelligence Technology and Applications",
}