Advantages and limitations of reservoir computing on model learning for robot control

Research output: Contribution to conferencePaper, not in proceeding

Abstract

In certain cases analytical derivation of physicsbased models of robots is difficult or even impossible. A potential workaround is the approximation of robot models from
sensor data-streams employing machine learning approaches.
In this paper, the inverse dynamics models are learned by
employing a learning algorithm, introduced in [1], which is
based on reservoir computing in conjunction with self-organized
learning and Bayesian inference. The algorithm is evaluated
and compared to other state of the art algorithms in terms
of generalization ability, convergence and adaptability using
five datasets gathered from four robots in order to investigate
its pros and cons. Results show that the proposed algorithm
can adapt in real-time changes of the inverse dynamics model
significantly better than the other state of the art algorithms.

Details

Authors
External organisations
  • Aalborg University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Robotics
Original languageEnglish
Publication statusPublished - 2015
Publication categoryResearch
Peer-reviewedYes
Externally publishedYes
Event2nd IROS Workshop on Machine Learning in Planning and Control of Robot Motion - Hamburg, Germany
Duration: 2015 Oct 22015 Oct 2

Workshop

Workshop2nd IROS Workshop on Machine Learning in Planning and Control of Robot Motion
CountryGermany
CityHamburg
Period2015/10/022015/10/02