SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Running Across the Reality Gap: Octopod Locomotion Evolved in a Minimal Simulation
Proceedings of the First European Workshop on Evolutionary Robotics
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Evolving coordinated quadruped gaits with the HyperNEAT generative encoding
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Crossing the reality gap in evolutionary robotics by promoting transferable controllers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Analysis of a neural oscillator
Biological Cybernetics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolving flexible joint morphologies
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Evolution of station keeping as a response to flows in an aquatic robot
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Evolution has produced a wide variety of organisms that interact with their physical environment through musculoskeletal systems. Movements are often aided by passive characteristics of an organism's body and the inherent flexibility of muscles. Emulating these characteristics in a robot can potentially increase performance and maneuverability, but requires finding effective solutions among an infinite set of possible morphology and controller combinations. Evolutionary computation provides a means to explore this large search space. However, developing simulation models to account for these material properties presents challenges. In this paper, we present an overview of the challenges in implementing such an evolutionary approach. We also present preliminary results demonstrating the effectiveness of our proposed methods.