Evolution of adaptive center-crossing continuous time recurrent neural networks for biped robot control

We used simulated evolution to obtain continuous time recurrent neural networks to control the locomotion of simulated bipeds. We also used the definition of center-crossing networks, so that the recurrent networks nodes can reach their areas of maximum sensitivity of their activation functions. Moreover, we incorporated a run-time adaptation of the nodes' biases to obtain such condition. We tested the improvements and possibilities this adaptation adds, focusing in the use for biped robot control.