An associator network approach to robot learning by imitation through vision, motor control and language

Abstract

Imitation learning offers a valuable approach for developing intelligent robot behaviour. We present an imitation approach based on an associator neural network inspired by brain modularity and mirror neurons. The model combines multimodal input based on higher-level vision, motor control and language so that a simulated student robot is able to learn from observing three behaviours which are performed by a teacher robot. The student robot associates these inputs to recognise the behaviour being performed or to perform behaviours by language instruction. With behaviour representations segregating into regions it models aspects of the mirror neuron system as similar patterns of neural activation are involved in recognition and performance.