A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation

Abstract

Developing neuro-inspired computing paradigms that mimic nervous system function is an emerging field of research that fosters our model understanding of the biological system and targets technical applications in artificial systems. The computational power of simulated brain circuits makes them a very promising tool for the development for brain-controlled robots. Early phases of robotic controllers development make extensive use of simulators as they are easy, fast and cheap tools. In order to develop robotics controllers that encompass brain models, a tool that include both neural simulation and physics simulation is missing. Such a tool would require the capability of orchestrating and synchronizing both simulations as well as managing the exchange of data between them. The Neurorobotics Platform (NRP) aims at filling this gap through an integrated software toolkit enabling an experimenter to design and execute a virtual experiment with a simulated robot using customized brain models. As a use case for the NRP, the iCub robot has been integrated into the platform and connected to a spiking neural network. In particular, experiments of visual tracking have been conducted in order to demonstrate the potentiality of such a platform.