Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Omnidirectional Locomotion for Quadruped Robots
RoboCup 2001: Robot Soccer World Cup V
Combining Simulation and Reality in Evolutionary Robotics
Journal of Intelligent and Robotic Systems
Autonomous Learning of Stable Quadruped Locomotion
RoboCup 2006: Robot Soccer World Cup X
Efficient Walking Speed Optimization of a Humanoid Robot
International Journal of Robotics Research
Reinforcement learning for robot soccer
Autonomous Robots
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Portable autonomous walk calibration for 4-legged robots
Applied Intelligence
Pruning neural networks for a two-link robot control system
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Highly modular architecture for the general control of autonomous robots
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
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This paper presents a new approach to optimize gait parameter sets using evolutionary algorithms. It separates the crossover-step of the evolutionary algorithm into an interpolating step and an extrapolating step, which allows for solving optimization problems with a small population, which is an essential for robotics applications. In contrast to other approaches, odometry is used to assess the quality of a gait. Thereby, omni-directional gaits can be evolved. Some experiments with the Sony Aibo models ERS-210 and ERS-7 prove the performance of the approach including the fastest gait found so far for the Aibo ERS-210.