Autonomous and fast robot learning through motivation

M. Rodríguez, R. Iglesias, C. V. Regueiro, J. Correa, S. Barro

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. In this context we propose a methodology which combines reinforcement learning and genetic algorithms to teach a robot how to perform a task when only the specification of the main restrictions of the desired behaviour is provided. Through this combination, both paradigms must be merged in such a way that they influence each other to achieve a fast convergence towards a good robot-control policy, and reduce the random explorations the robot needs to carry out in order to find a solution. Another advantage of our proposal is that it is able to easily incorporate any kind of domain-dependent knowledge about the task. This is very useful for improving a robot controller, for applying a robot-controller to move a different robot-platform, or when we have certain "feelings" about how the task should be solved. The performance of our proposal is shown through its application to solve a common problem in mobile robotics.

Original languageEnglish
Pages (from-to)735-740
Number of pages6
JournalRobotics and Autonomous Systems
Volume55
Issue number9
DOIs
StatePublished - Sep 30 2007
Externally publishedYes

Keywords

  • Autonomous agents
  • Genetic algorithms
  • Reinforcement learning
  • Robot control

Fingerprint

Dive into the research topics of 'Autonomous and fast robot learning through motivation'. Together they form a unique fingerprint.

Cite this