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A biologically motivated paradigm for heuristic motor control in multiple contexts

A biologically motivated controller is presented and investigated in terms of motor control and motor learning, especially motor learning of different behaviors. The controller, based on several ideas presented in the literature, used multiple paired inverse and forward internal models to generate motor commands and estimate the current environment within which the controller operated. The controller demonstrated an ability to control a simulated arm-like manipulator making 20cm reaching movements with an RMS position error of 4.6mm while making unperturbed movements, and 6.40mm while making the same reaching movements within a curl field. However, the ability to learn and retain multiple motor behaviors was proven not to be possible with the current paradigm and its learning algorithm. The results of the investigation were viewed in the scope of motor control for engineering purposes, and for use in developing a model for human motor control to further the science of neuro-recovery. Thesis Supervisor: Neville Hogan Title: Professor