Self-Organization of Exploratory Control

Animals process sensory information in order to generate behavior that matches the affordances of their environment. The statistical properties of an environment, however, appear substantially different to an inexperienced individual and to its more skilled fellow. On the one hand, appropriate learning opportunities may rarely occur by chance, while on the other hand a concentration on well-learned behaviors tends to reduce the frequency of errors although these are important indicators of the environmental dynamics. In order to extract the currently relevant aspects of the sensory world, the agent should thus aim at performing coherent behavior based on incident regularities as well as maintain a constant excitation of the controlled systems in order to escape from suboptimal behaviors and track the environmental dynamics. The strategies for departing from either of these uninformative situations requires the agent to engage actively in the learning process [1]. In the present contribution we will show that homeokinetic learning [2] is an expedient method to achieve this goal and that it presents a viable approach to active learning. This method comprises the simultaneous maximization of the sensitivity of an agent with respect to its stimuli and the predictability of these stimuli. Sensitization leads to exploratory behavior which is counterbalanced by the requirement of predictability by means of an internal model. As a theoretically provable consequence, the motor activity becomes distributed over many degrees of freedom of a robotic system. Homeokinetic learning leads to flexible, versatile and body-specific behaviors [3] that can be characterized by their controllability.