SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Walking: a complex behavior controlled by simple networks
Adaptive Behavior - Special issue on computational neuroethology
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
Automated synthesis and optimization of robot configurations: an evolutionary approach
Automated synthesis and optimization of robot configurations: an evolutionary approach
Evolving modular genetic regulatory networks
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
How robot morphology and training order affect the learning of multiple behaviors
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
On the relationship between environmental and morphological complexity in evolved robots
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolving Symmetry for Modular System Design
IEEE Transactions on Evolutionary Computation
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This paper proposes an approach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimizing a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.