Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation

Research output: Contribution to journalArticleResearchpeer-review

Authors

External Organisational units

  • Technische Universität Graz
  • Technische Universität Darmstadt

Abstract

One key idea behind morphological computation is that many difficulties of a control problem can be absorbed by the morphology of a robot. The performance of the controlled system naturally depends on the control architecture and on the morphology of the robot. Because of this strong coupling, most of the impressive applications in morphological computation typically apply minimalistic control architectures. Ideally, adapting the morphology of the plant and optimizing the control law interact so that finally, optimal physical properties of the system and optimal control laws emerge. As a first step toward this vision, we apply optimal control methods for investigating the power of morphological computation. We use a probabilistic optimal control method to acquire control laws, given the current morphology. We show that by changing the morphology of our robot, control problems can be simplified, resulting in optimal controllers with reduced complexity and higher performance. This concept is evaluated on a compliant four-link model of a humanoid robot, which has to keep balance in the presence of external pushes.

Details

Original languageEnglish
Pages (from-to)115–131
Number of pages17
JournalArtificial Life
Volume19.2013
Issue number1
DOIs
Publication statusPublished - Jan 2013
Externally publishedYes