MASSIVELY PARELLEL REAL-TIME NETWORK ARCHITECTURES FOR ROBOTS CAPABLE OF SELF-CALIBRATING THEIR OPERATING PARAMETERS THROUGH ASSOCIATIVE LEARNING

A real-time network enables robots to accurately learn sensory motor transformation and to self-train and self-calibrate operating parameters after accidents or with wear. Combinations of visual and present position signals are used to relearn a target position map. Target positions in body-centered. visually activated coordinates are mapped into target positions in motor coordinates which are compared with present positions in motor coordinates to generate motor commands. Feedback provides calibrated error signals for adjustment of learned gain with changes in the system due to aging, accidents and the like. A series of prestored motor commands may be performed with a later "go" command.