Encoding Complete Body Models Enables Task Dependent Optimal Behavior

Many neural network models of (human) motor learning focus on the acquisition of direct goal-to-action mappings, which results in rather inflexible motor control programs. We propose a neural network architecture (SURE_REACH) that acquires complete body models through unsupervised learning. It encodes redundancy on the kinematic and on the motor command level in order to exert highly flexible, task-dependent optimal control. This paper shows that our approach accounts for two forms of effective human behavior based on exploiting kinematic redundancy. First, depending on the starting posture, hand targets are pursued in different ways optimizing movement efficiency. Second, the arm posture at the end of a movement can be aligned anticipatorily to facilitate a subsequent movement. A discussion of computational implications and relations to behavioral and neurophysiological findings concludes the paper.