Autonomous learning in humanoid robotics through mental imagery

作者：Di Nuovo Alessandro G^*; Marocco Davide; Di Nuovo Santo; Cangelosi Angelo

来源：Neural Networks, 2013, 41: 147-155.

DOI：10.1016/j.neunet.2012.09.019

• Recurrent neural networks
• Mental imagery
• Autonomous learning
• Ballistic movement
• Mental training

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.

• 出版日期2013-5