A movement pattern generator model using artificial neural networks

The authors have developed a movement pattern generator, using an artificial neural network (ANN) for generating periodic movement trajectories. This model is based on the concept of 'central pattern generators'. Jordan's (1986) sequential network, which is capable of learning sequences of patterns, was modified and used to generate several bipedal trajectories (or gaits), coded in task space, at different frequencies. The network model successfully learned all of the trajectories presented to it. The model has many attractive properties, such as limit cycle behavior, generalization of trajectories and frequencies, phase maintenance, and fault tolerance. The movement pattern generator model is potentially applicable for improved understanding of animal locomotion and for use in legged robots and rehabilitation medicine.<
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