A cerebellar model of timing and prediction in the control of reaching
Neural Computation
Searching a Scalable Approach to Cerebellar Based Control
Applied Intelligence
Real time dynamics and control of a digital human arm for reaching motion simulation
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
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Biological control systems have long been studied as possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. In this paper, we present a model of cerebellar control of a muscle-actuated, two-link, planar arm. The model learns in a trial-and-error fashion to generate the appropriate sequence of motor signals that accurately bring the arm to a specified target. The motor signals produced by the cerebellum are specified in muscle synergy space. When the cerebellum fails to bring the arm to the target, an extra-cerebellar module performs low-quality corrective movements, from which the cerebellum updates its program. In learning to perform the task, the cerebellum constructs an implicit inverse model of the plant. This model uses a combination of delayed sensory signals and recently-generated motor commands to compute the new output motor signal.