A SELF-ORGANIZED MODEL FOR THE CONTROL, PLANNING AND LEARNING OF NONLINEAR MULTIDIMENSIONAL SYSTEMS USING A SENSORY FEEDBACK

A new approach is presented to deal with the problem of modelling and simulating the control mechanisms underlying planned-arm-movements. We adopt a synergetic view in which we assume that the movement patterns are not explicitly programmed but rather are emergent properties of a dynamic system constrained by physical laws in space and time. The mo del automatically translates a high-level command specification into a complete movement trajectory. This is an inverse problem, since the d ynamic variables controlling the current state of the system have to b e calculated from movement outcomes such as the position of the arm en dpoint. The proposed method is based on an optimization strategy: the dynamic system evolves towards a stable equilibrium position according to the minimization of a potential function. This system, which could well be described as a feedback control loop, obeys a set of non-line ar differential equations. The gradient descent provides a solution to the problem which proves to be both numerically stable and computatio nally efficient. Moreover, the addition into the control loop of eleme nts whose structure and parameters have a pertinent biological meaning allows for the synthesis of gestural signals whose global patterns ke ep the main invariants of human gestures. The model can be exploited t o handle more complex gestures involving planning strategies of moveme nt. Finally, the extension of the approach to the learning and control of non-linear biological systems is discussed.