First published 2 September 2013
Ballistic Action Planning in Robotics by means of Artificial Imagery
Paolo Arena, Luca Patanè, Roland Strauss
This abstract aims to present our recent work on exploring the concept of mental imagery and mental simulation as a fundamental cognitive capability applied to robot controllers, with the aim of improving the motor performance of the robot in terms of motor control and multi-degrees of freedom coordination. Indeed we believe that mental imagery models can give the opportunity to apply such behaviour toward the development of artificial cognitive systems, in order to improve robots' motor performance in general and in complex motor planning. This objective can be achieved using bio-inspired computational modelling technologies, such as artificial recurrent neural networks, able to emulate processes of mental training by mental simulation.
In particular, as proof-of-concept, we designed a dual neural network architecture, that allows the iCub to improve autonomously its sensorimotor skills, with techniques inspired by the ones that are employed with human subjects in sports training. This is achieved by endowing a feedforward controller of a secondary recurrent 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 additional learning process. Moreover we show that data obtained with artificial imagination could be used to simulate mental training to learn new tasks and enhance their performance. Results of experimental tests in controlling a ballistic movement with the simulator of the iCub humanoid robot platform are presented as evidence of the opportunities presented by the use of artificial mental imagery in cognitive robotics.