Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolving neural networks through augmenting topologies
Evolutionary Computation
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
MAVisto: a tool for the exploration of network motifs
Bioinformatics
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
Revising the evolutionary computation abstraction: minimal criteria novelty search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Study on evolution of the artificial flying creature controlled by neuro-evolution
Artificial Life and Robotics
Effective diversity maintenance in deceptive domains
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Fluid bipedal locomotion remains a significant challenge for humanoid robotics. Recent bio-inspired approaches have made significant progress by using small numbers of tightly coupled neurons, called central pattern generators (CPGs). Our approach exchanges complexity of the neuron model for complexity of the network, gradually building a network of simple neurons capable of complex behaviors. We show this approach generates controllers de novo that are able to control 3D bipedal locomotion up to 10 meters. This results holds for robots with human-proportionate morphologies across 95% of normal human variation. The resulting networks are then examined to discover neural structures that arise unusually often, lending some insight into the workings of otherwise opaque controllers.