Development of goal-oriented behavior in self-learning robots
Georg Martius, J. Michael Herrmann · BMC Neuroscience · 2011
The homeokinetic principle [1] describes a mechanism for the self-organisation of behaviour in early development. It implies a self-tuned balance between the sensitivity of motor actions with respect to sensory inputs and the predictability of the perceptual consequences of actions. The principle gives rise to a synaptic plasticity rule for artificial motor neurons, which has been shown to generate coherent and coordinated movements in autonomous robots [2] and which can be interpreted as a model of early behavioural learning. Learning in this sense consists in the construction of a behavioural manifold which must, however, remain modifiable in order to incorporated goal-related information or rewards in the course of further development. Goal-related optimization for shaping rather than replacing on-going exploration is referred to as guided self-organisation and is the subject of the present paper. We present three strategies for guided self-organization, namely using rewards, teaching signals or assumptions about the symmetries of the desired behaviour. The strategies are analysed for several different robots (a khepera-like robot, spherical robot, snake, and multi-segment chain robot) in a physically realistic simulation [2].