Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics
Proceedings of the Third European Conference on Advances in Artificial Life
Evolutionary Robotics: A Survey of Applications and Problems
Proceedings of the First European Workshop on Evolutionary Robotics
Ideal Evaluation from Coevolution
Evolutionary Computation
Action-selection and crossover strategies for self-modeling machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Linear Genetic Programming
Incremental evolution of target-following neuro-controllers for flapping-wing animats
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Nonlinear System Identification Using Coevolution of Models and Tests
IEEE Transactions on Evolutionary Computation
Dynamic page based crossover in linear genetic programming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Crossing the reality gap in evolutionary robotics by promoting transferable controllers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification
Journal of Intelligent and Robotic Systems
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In evolutionary robotics, controllers are often designed in simulation, then transferred onto the real system. Nevertheless, when no accurate model is available, controller transfer from simulation to reality means potential performance loss. It is the reality gap problem. Unmanned aerial vehicles are typical systems where it may arise. Their locomotion dynamics may be hard to model because of a limited knowledge about the underlying physics. Moreover, a batch identification approach is difficult to use due to costly and time consuming experiments. An automatic identification method is then needed that builds a relevant local model of the system concerning a target issue. This paper deals with such an approach that is based on coevolution of models and tests. It aims at improving both modeling and control of a given system with a limited number of manipulations carried out on it. Experiments conducted with a simulated quadrotor helicopter show promising initial results about test learning and control improvement.