Sensorimotor models were acquired in three setups: a kinematic arm model (section 4.5), a real robot arm equipped with a camera (chapter 6), and mobile robot also equipped with a camera (chapter 7). The raw data contained the following:
Training data were collected during random exploration; that is, the robot chose random motor commands and observed the sensory consequences of its actions. This exploration is the actual unsupervised part of the learning. Whether the following interpretation of this data is called unsupervised is more a matter of definition (see, for example, Meinicke (2000)).
Different from the kinematic arm model, the studies with the real robots revealed that it was necessary to preprocess the collected data. The dimensionality of the images was too high. The following three processing strategies proved to be useful:
A processing that extracts lines, rectangles, or the like from an image was avoided. Including such more complex sensory representations may lead to the same conceptual problems as discussed for the symbolic approach (section 1.4.1). These representations depend on the designer's choice and may distract from the real difficulties of a behavioral task (Möller, 1999; Brooks, 1986b).