Modelling of direct motor program learning in fast human arm motions

We propose and simulate a new paradigm for organization of motor control in fast and accurate human arm motions. We call the paradigm "direct motor program learning" since the control programs are learned directly without knowing or learning the dynamics of a controlled system. The idea is to approximate the dependence of the motor control programs on the vector of the task parameters rather than to use a model of the system dynamics. We apply iterative learning control and scattered data multivariate approximation techniques to achieve the goal. The advantage of the paradigm is that the control complexity depends neither on the order nor on the nonlinearity of the system dynamics. We simulate the direct motor program learning paradigm in the task of point-to-point control of fast planar human arm motions. Simulation takes into account nonlinear arm dynamics, muscle force dynamics, delay in low-level reflex feedback, time dependence of the feedback gains and coactivation of antagonist muscles. Despite highly nonlinear time-variant dynamics of the controlled system, reasonably good motion precision is obtained over a wide range of the task parameters (initial and final positions of the arm). The simulation results demonstrate that the paradigm is indeed viable and could be considered as a possible explanation for the organization of motor control of fast motions.