Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars.

ABSTRACT: Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture--specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot's motor internal state, (iii) posture recognition, and (iv) novelty detection--is able to learn from an interactive experience involving mutual imitation. This mutual i

SUBMITTER: Boucenna S 

PROVIDER: S-EPMC4741103 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature