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Gait synthesis of a biped robot using backpropagation through time algorithm

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2 Author(s)
Jih-Gau Juang ; Dept. of Electr. Eng., Missouri Univ., Columbia, MO, USA ; Chun-shin Lin

A neural network architecture is developed for the gait synthesis of a five-link biped walking robot. The learning scheme uses a multilayered feedforward neural network combined with a linearized inverse biped model. It can generate walking gait by giving reference trajectory which defines a desired gait in several stages. The algorithm used to train network is known as back-propagation with time-delay or so-called backpropagation through time. A three-layered neural network is used as a controller, it provides the control signals in each stage of a walking gait. The linearized inverse biped model calculates the error signals which will be used to back propagate through the controller in each stage

Published in:

Neural Networks, 1996., IEEE International Conference on  (Volume:3 )

Date of Conference:

3-6 Jun 1996