Approximation capabilities of multilayer feedforward networks
Neural Networks
Stabilization of biped dynamic walking using gyroscopic couple
IJSIS '96 Proceedings of the 1996 IEEE International Joint Symposia on Intelligence and Systems
Fuzzy neural network approaches for robotic gait synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Advanced Intelligence Paradigms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Near-optimal gait generations of a two-legged robot on rough terrains using soft computing
Robotics and Computer-Integrated Manufacturing
Effects of using different neural network structures and cost functions in locomotion control
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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A learning scheme is developed to generate robotic locomotion on different sloping surfaces. This scheme uses three neural networks: a neural network controller, a neural network emulator, and a slope information neural network. The neural network controller is pre-trained by a reference trajectory on a horizontal surface. The emulator is pre-trained to identify the robotic dynamics. The slope information neural network provides compensated control signals to the robot on different slope angles by using the control signals on the horizontal surface from the pre-trained controller. The training rule is a backpropogation algorithm with time delay. The proposed technique can generate gaits on different sloping surfaces by following a reference trajectory with desired step length, crossing height and walking speed.