title: Autonomous learning in humanoid robotics through mental imagery.
abstract: In this paper we focus on modeling autonomous learning to improve performance of a humanoid robot through a modular artificial neural networks architecture. A model of a neural controller is presented, which allows a humanoid robot iCub to autonomously improve its sensorimotor skills. This is achieved by endowing the neural controller with a secondary neural system that, by exploiting the sensorimotor skills already acquired by the robot, is able to generate additional imaginary examples that can be used by the controller itself to improve the performance through a simulated mental training. Results and analysis presented in the paper provide evidence of the viability of the approach proposed and help to clarify the rational behind the chosen model and its implementation.
authors.affiliation: Centre for Robotics and Neural Systems, School of Computing and Mathematics, Plymouth University, UK. alessandro.dinuovo@plymouth.ac.uk
authors.forename: Alessandro G
authors.initials: AG
authors.lastname: Di Nuovo
authors.forename: Davide
authors.initials: D
authors.lastname: Marocco
authors.forename: Santo
authors.initials: S
authors.lastname: Di Nuovo
authors.forename: Angelo
authors.initials: A
authors.lastname: Cangelosi
mesh_terms: D001185:Artificial Intelligence; D005246:Feedback; D006801:Humans; D007092:Imagination; D008962:Models, Theoretical; D009048:Motor Skills; D009068:Movement; D016571:Neural Networks, Computer; D010465:Perception; D012371:Robotics / Q000379:methods*