Legged robots that balance
Understanding intelligence
Computational principles of mobile robotics
Computational principles of mobile robotics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Automatic Generation of Control Programs for Walking Robots Using Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A method for isolating morphological effects on evolved behaviour
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Autonomous Robots: From Biological Inspiration to Implementation and Control (Intelligent Robotics and Autonomous Agents)
Neural Networks Applied to Gait Control of Physically Based Simulated Robots
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Applying Genetic Algorithms to Control Gait of Simulated Robots
CERMA '07 Proceedings of the Electronics, Robotics and Automotive Mechanics Conference
Gait control generation for physically based simulated robots using genetic algorithms
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
An analysis of neural models for walking control
IEEE Transactions on Neural Networks
A review of gait optimization based on evolutionary computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
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This paper describes our research and experiments with autonomous robots, in which were used genetic algorithms to evolve stable gaits of simulated legged robots in a physically based simulation environment. In our approach, gaits are defined using two different methods: a finite state machine based on the joint angles of the robot legs; and an Elman's recurrent neural network. The parameters for both methods are optimized using genetic algorithms, and the proposed model also allows the evolution of the robot body morphology. Several experiments are described, and the obtained results show that it is possible to generate stable gaits and efficient morphologies using machine learning techniques.