Learning to reach after learning to look: A study of autonomy in learning sensorimotor transformations

A computing architecture based on neuronal principles is presented, which implements learning to reach towards visually-perceived targets for an embodied agent. The whole behavioural loop from object perception to motor control is realised in the architecture using attractor dynamics and Dynamic Neural Fields. The sensory-motor mappings, involved in generation of saccadic gaze shifts and goal-directed arm movements, adapt in the system autonomously during the behaviour. A network of neural-dynamic nodes organises activation and deactivation of the behavioural modules of the architecture, leading to an autonomous process model of learning to look and to reach. The architecture was implemented and validated on a simulated robot.