Learning versatile sensorimotor coordination with goal babbling and neural associative dynamics

We combine an efficient exploration mechanism for bootstrapping of sensorimotor coordination skills with an associative network model to represent multiple coordination styles. Both approaches are integrated into a three-phased process of exploration, i.e. discovery of a new model, consolidation, the long-term storage of multiple models in a dynamical associative network, and exploitation of multiple models by the neural dynamics for versatile sensorimotor coordination. The proposed exploration-consolidation process is demonstrated for a planar robotic manipulator with ten degrees of freedom. Exploitation of sensorimotor coordination from the consolidated neural dynamics features motor hysteresis and additionally comprises a forward model that can be utilized to interpret proprioceptive feedback.