Modular Neuroevolution for Multilegged Locomotion (2008)
Author: Vinod Valsalam
Controllers for multilegged robots can be represented as modular neural networks and optimized using evolution. Experiments using physically realistic simulations of a quadruped robot produce the same symmetric gaits, such as pronk, pace, bound and trot, that are seen in quadruped animals. Moreover, the robot can transition dynamically to more effective gaits when faced with obstacles. The modular approach also scales well when the number of legs or their degrees of freedom are increased. Evolved non-modular controllers, in contrast, produce gaits resembling crippled animals that are much less effective and do not scale up as a result. Hand-designed controllers are also less effective, especially on an obstacle terrain. These results suggest that the modular approach is effective for designing robust locomotion controllers for multilegged robots.

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Risto Miikkulainen Faculty risto [at] cs utexas edu
Vinod Valsalam Ph.D. Alumni vkv [at] alumni utexas net
Modular Neuroevolution for Multilegged Locomotion Vinod K. Valsalam and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2008, 265-272, Ne... 2008