The Problem of Adaptive Control in a Living System or How to Acquire an Inverse Model Without External Help

Recent research uncovers that goal directed sensorimotor behaviour is governed by negative feedback of positional error, and by feedforward through inverse modelling of the limb’s dynamics. Thereby, forward models seem to provide the kinematic state of the limb. The question addressed in the paper is, how the neural network representing the inverse model can be trained. Because in this case an error based learning algorithm seems to be unavailable, an alternative non error based method called auto-imitation is proposed. It is demonstrated, that, if combining a special type of neural network (the power net) with a modified type of a Hebbian synapse, the inverse dynamics of an onejointed arm can be precisely identified using auto-imitation. This holds for a simulated arm and a real robot arm as well.