Stochastic Optimal Control Methods for Investigating the Power of Morphological Computation
- 1 January 2013
- journal article
- Published by MIT Press in Artificial Life
- Vol. 19 (1), 115-131
- https://doi.org/10.1162/artl_a_00085
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.This publication has 9 references indexed in Scilit:
- Optimal control as a graphical model inference problemMachine Learning, 2012
- Towards a theoretical foundation for morphological computation with compliant bodiesBiological Cybernetics, 2011
- Self-Organization, Embodiment, and Biologically Inspired RoboticsScience, 2007
- Multiple balance strategies from one optimization criterionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Sensing through body dynamicsRobotics and Autonomous Systems, 2006
- How the Body Shapes the Way We ThinkPublished by MIT Press ,2006
- Incremental Online Learning in High DimensionsNeural Computation, 2005
- A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES)Evolutionary Computation, 2003