Evolving Locomotion Controllers for Multilegged Robots
Active from 2008 - 2010
Designing stable and robust controllers for multilegged robots is a challenging task, and it would therefore be desirable to develop automated methods for doing it. Learning the control behavior is difficult however because optimal behavior is not known, and the search space is too large for reinforcement learning and for straightforward evolution. As a potential solution, this project uses an approach called Evolution of Network Symmetry and mOdularity (ENSO). ENSO generates appropriate gaits for a given robot and environment by utilizing a mathematical model of animal locomotion and group-theoretic analysis of controller symmetry. It represents the controllers as interconnected neural network modules and optimizes module functionality and interconnection symmetry using evolution. In this project, this approach is evaluated by evolving controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as fast as those having hand-designed symmetry, and significantly faster than those without symmetry. On inclined ground, where the appropriate symmetries are difficult to determine manually, ENSO produced significantly faster gaits that also generalize better than those of other approaches. On robots with a more complicated structure including knee joints, ENSO resulted in more regular gaits than the other approaches. These results suggest that ENSO can be used to design effective locomotion controllers for multilegged robots.

Videos of evolved walking behaviors
Vinod Valsalam Ph.D. Alumni vkv [at] alumni utexas net
Risto Miikkulainen Faculty risto [at] cs utexas edu
Constructing Controllers for Physical Multilegged Robots using the ENSO Neuroevolution Approach 2012
Vinod K. Valsalam, Jonathan Hiller, Robert MacCurdy, Hod Lipson and Risto Miikkulainen, Evolutionary Intelligence, Vol. 5, 1 (2012), pp. 1--12.
Evolving Symmetry for Modular System Design 2011
Vinod K. Valsalam and Risto Miikkulainen, IEEE Transactions on Evolutionary Computation, Vol. 15, 3 (2011), pp. 368--386.
Utilizing Symmetry in Evolutionary Design 2010
Vinod Valsalam, PhD Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI-10-04.
Evolving Symmetric and Modular Neural Network Controllers for Multilegged Robots 2009
Vinod K. Valsalam and Risto Miikkulainen, In xploring New Horizons in Evolutionary Design of Robots: Workshop at the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2009.
Evolving Symmetric and Modular Neural Networks for Distributed Control 2009
Vinod K. Valsalam and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 731--738 2009.
Modular Neuroevolution for Multilegged Locomotion 2008
Vinod K. Valsalam and Risto Miikkulainen, In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, pp. 265-272, New York, NY, USA 2008. ACM.
ENSO This package contains software implementing the ENSO approach for evolving symmetric modular neural networks. It also in... 2010