A Neural Network Model of an Anthropomorphic Arm

This paper introduces a neural network model of a planar redundant arm whose structure and operation principles were inspired by those of the human arm. We developed the model for two purposes. One purpose was to study the relative role of control strategies and plant properties in trajectory formation, namely which features of simple arm movements can be attributed to the properties of the plant alone. We address this matter in a companion paper [1]. The second purpose was a motor-learning one: to design an arm model that, because of its neural-network quality, can be eventually incorporated in a parallel distributed learning scheme for the arm controller. We modeled the arm with two joints (shoulder and elbow) and six muscle-like actuators: a pair of antagonist shoulder muscles, a pair of antagonist elbow muscles and a pair of antagonist double-joint muscles. The arm was allowed to move in the horizontal plane subject to the action of gravity. The model computes the transformation between the control signals that activate the muscle-like actuators and the coordinates of the arm endpoint. This transformation comprises four interacting stages (muscle dynamics, joint geometry, forward arm dynamics, forward arm kinematics) that we modeled with a number of feedforward and recurrent neural networks. In this paper we introduce and describe in detail the modeling methods, that are e cient, highly exible (some of the resulting networks can be easily modi ed to accommodate di erent parametric choices and temporal scales), and quite general and hence applicable to a number of di erent scienti c domains.