On the dynamics of small continuous-time recurrent neural networks
Adaptive Behavior - Special issue on computational neuroethology
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots
Biologically inspired robot behavior engineering
Locomotion Control of a Biped Robot Using Nonlinear Oscillators
Autonomous Robots
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Reflex-oscillations in evolved single leg neurocontrollers for walking machines
Natural Computing: an international journal
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Efference copies in neural control of dynamic biped walking
Robotics and Autonomous Systems
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
We used center-crossing continuous time recurrent neural networks as central pattern generator controllers in biped robots, together with an adaptive methodology to improve the ability of the recurrent neural networks to produce rhythmic activation behaviors. The parameters of the recurrent networks are adapted or modified in run-time to reach the center-crossing condition, so the nodes get close to the most sensitive region to their input. This facilitates the evolution of the networks that act as central pattern generators to control biped structures. The robustness of the adaptive networks to produce rhythmic activation patterns was checked as well as the improvements and possibilities this adaptation may add.