To read this content please select one of the options below:

Vision processing for robot learning

Ulrich Nehmzow (Lecturer in Artificial Intelligence and Robotics, Department of Computer Science, University of Manchester, UK)

Industrial Robot

ISSN: 0143-991x

Article publication date: 1 March 1999

344

Abstract

Robot learning ‐ be it unsupervised, supervised or self‐supervised ‐ is one method of dealing with noisy, inconsistent, or contradictory data that has proven useful in mobile robotics. In all but the simplest cases of robot learning, raw sensor data cannot be used directly as input to the learning process. Instead, some “meaningful” preprocessing has to be applied to the raw data, before the learning controller can use the sensory perceptions as input. In this paper, two instances of supervised and unsupervised robot learning experiments, using vision input are presented. The vision sensor signal preprocessing necessary to achieve successful learning is also discussed.

Keywords

Citation

Nehmzow, U. (1999), "Vision processing for robot learning", Industrial Robot, Vol. 26 No. 2, pp. 121-130. https://doi.org/10.1108/01439919910260204

Publisher

:

MCB UP Ltd

Copyright © 1999, MCB UP Limited

Related articles