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Inferring door locations from a teammate’s trajectory in stealth human-robot team operations

Jean Hyaejin Oh, Luis Ernesto Navarro-Serment, Arne Suppe, Anthony (Tony) Stentz and Martial Hebert
Carnegie Mellon University, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October, 2015

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Abstract

Robot perception is generally viewed as the interpretation of data from various types of sensors such as cameras. In this paper, we study indirect perception where a robot can perceive new information by making inferences from non-visual observations of human teammates. As a proof-of-concept study, we specifically focus on a door detection problem in a stealth mission setting where a team operation must not be exposed to the visibility of the team’s opponents. We use a special type of the Noisy-OR model known as BN2O model of Bayesian inference network to represent the inter-visibility and to infer the locations of the doors, i.e., potential locations of the opponents. Experimental results on both synthetic data and real person tracking data achieve an F-measure of over .9 on average, suggesting further investigation on the use of non-visual perception in human-robot team operations.

BibTeX Reference
@conference{Oh-2015-6034,
title = {Inferring door locations from a teammate’s trajectory in stealth human-robot team operations},
author = {Jean Hyaejin Oh and Luis Ernesto Navarro-Serment and Arne Suppe and Anthony (Tony) Stentz and Martial Hebert},
booktitle = {Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2015},
address = {Pittsburgh, PA},
}
2017-09-13T10:38:33+00:00