By Topic

An assessment of machine learning methods for robotic discovery

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Bratko, I. ; Fac. of Comput. & Info. Sc., Univ. of Ljubljana, Ljubljana

In this paper we consider autonomous robot discovery through experimentation in the robotpsilas environment. We analyse the applicability of machine learning (ML) methods with respect to various levels of robot discovery tasks, from extracting simple laws among the observed variables, to discovering completely new notions that were never mentioned in the data directly. We first introduce the XPERO project, and present some illustrative initial experiments in robot learning in XPERO. Then we formulate a systematic list of types of learning or discovery tasks, and discuss the suitability of chosen ML methods for these tasks.

Published in:

Information Technology Interfaces, 2008. ITI 2008. 30th International Conference on

Date of Conference:

23-26 June 2008