A self-organized model for the control, planning and learning of nonlinear multi-dimensional systems using a sensory feedback

摘要

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 model automatically translates a high-level command specification into a complete movement trajectory. This is an inverse problem, since the dynamic variables controlling the current state of the system have to be calculated from movement outcomes such as the position of the arm endpoint. 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-linear differential equations. The gradient descent provides a solution to the problem which proves to be both numerically stable and computationally efficient. Moreover, the addition into the control loop of elements whose structure and parameters have a pertinent biological meaning allows for the synthesis of gestural signals whose global patterns keep the main invariants of human gestures. The model can be exploited to handle more complex gestures involving planning strategies of movement. Finally, the extension of the approach to the learning and control of non-linear biological systems is discussed.