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Semi-task-dependent and uncertainty-driven world model maintenance

Semi-task-dependent and uncertainty-driven world model maintenance Nearly every task a domestic robot could potentially solve requires a description of the robot’s environment which we call a world model. One problem underexposed in the literature is the maintenance of world models. Rather than on creating a world model, this work focuses on finding a strategy that determines when to update which object in the world model. The decision whether or not to update an object is based on the expected information gain obtained by the update, the action cost of the update and the task the robot performs. The proposed strategy is validated during both simulations and real world experiments. The extended series of simulations is performed to show both the performance gain with respect to a benchmark strategy and the effect of the various parameters. The experiments show the proposed approach on different set-ups and in different environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Semi-task-dependent and uncertainty-driven world model maintenance

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References (39)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/s10514-014-9393-0
Publisher site
See Article on Publisher Site

Abstract

Nearly every task a domestic robot could potentially solve requires a description of the robot’s environment which we call a world model. One problem underexposed in the literature is the maintenance of world models. Rather than on creating a world model, this work focuses on finding a strategy that determines when to update which object in the world model. The decision whether or not to update an object is based on the expected information gain obtained by the update, the action cost of the update and the task the robot performs. The proposed strategy is validated during both simulations and real world experiments. The extended series of simulations is performed to show both the performance gain with respect to a benchmark strategy and the effect of the various parameters. The experiments show the proposed approach on different set-ups and in different environments.

Journal

Autonomous RobotsSpringer Journals

Published: Jul 2, 2014

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