According to one of the most influential principles of motor development theory, the circular-reaction hypothesis, infants perform exploratory random movements (motor babbling) to acquire efferent-reafferent associations later used to perform goal directed behavior. The models proposed so far to specify this principle learn to accomplish reaching tasks by using exploratory movements to associate final arm's postures with stimuli. A limit of these models is that they cannot control the path followed by the hand to arrive to the target and so cannot cope with obstacles. This work proposes a model that starts to overcome this limitation, in particular it proposes a new neural-network architecture that uses motor babbling not to learn stimuli-final postures associations but to learn stimuli-trajectories associations through an Hebb rule. The system controls movement trajectories by regulating the parameters of two Time Based Generators that on their turn generate the sequence of desired positions of the arms' "hand". These types of associations render the system more flexible and capable of coping with obstacles. Preliminary tests run with a 2D kinematic arm demonstrate the viability of the proposed approach.
Trajectory learning through motor babbling: reaching with obstacle avoidance
Contributo in atti di convegno
Quarto Workshop Italiano di Vita Artificiale e Computazione Evolutiva (WIVACE 2007), Catania, 5-7/09/2007