Simultaneously learning to recognize and control a low-cost robotic arm
Image and Vision Computing
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Multi-rate visual servoing based on dual-rate high order holds
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
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In this article we introduce a method to learn neural networks that solve a visual servoing task. Our method, called EANT, Evolutionary Acquisition of Neural Topologies, starts from a minimal network structure and gradually develops it further using evolutionary reinforcement learning. We have improved EANT by combining it with an optimisation technique called CMA-ES, Covariance Matrix Adaptation Evolution Strategy. Results from experiments with a 3 DOF visual servoing task show that the new CMAES based EANT develops very good networks for visual servoing. Their performance is significantly better than those developed by the original EANT and traditional visual servoing approaches.