Evolving Symmetric and Modular Neural Network Controllers for Multilegged Robots (2009)
Controllers for multilegged robots are characterized by modularity and symmetry. However, the controller symmetries necessary for generating appropriate gaits are often difficult to determine analytically. This paper utilizes a nature-inspired approach called Evolution of Network Symmetry and mOdularity (ENSO) to evolve such controllers automatically. It uses group theory to mutate symmetry systematically, making it more effective than mutating symmetry randomly. This approach was evaluated by evolving modular neural network controllers for a quadruped robot in physically realistic simulations. On flat ground, the resulting controllers are as effective as those having hand-designed symmetries. However, they are significantly faster when evolved on inclined ground, where the appropriate symmetries are difficult to determine manually. The group-theoretic symmetry mutations of ENSO are also significantly more effective at evolving such controllers than random symmetry mutations. Thus, ENSO is a promising approach for evolving modular and symmetric controllers for multilegged robots, as well as solutions to distributed control problems in general.
In xploring New Horizons in Evolutionary Design of Robots: Workshop at the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2009.

Risto Miikkulainen Faculty risto [at] cs utexas edu
Vinod Valsalam Ph.D. Alumni vkv [at] alumni utexas net