Akanyeti, O., Nehmzow, U. and Billings, S.A. (2008) Complex robot training tasks through bootstrapping system identification. Research Report. ACSE Research Report no. 982 . Automatic Control and Systems Engineering, University of Sheffield
Many sensor-motor competences in mobile robotics applications exhibit complex, non-linear characteristics. Previous research has shown that polynomial NARMAX models can learn such complex tasks. However as the complexity of the task under investigation increases, representing the whole relationship in one single model using only raw sensory inputs would lead to large models. Training such models is extremely difficult, and, furthermore, obtained models often exhibit poor performances.
This paper presents a bootsrapping method of generating complex robot training tasks using simple NARMAX models. We model the desired task by combining predefined low level sensor motor controllers. The viability of the proposed method is demonstrated by teaching a Scitos G5 autonomous robot to achieve complex route learning tasks in the real world robotics experiments.
|Copyright, Publisher and Additional Information:||The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.|
|Institution:||The University of Sheffield|
|Academic Units:||The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) > ACSE Research Reports|
|Depositing User:||Miss Anthea Tucker|
|Date Deposited:||16 Oct 2012 08:51|
|Last Modified:||07 Jun 2014 23:07|
|Publisher:||Automatic Control and Systems Engineering, University of Sheffield|