Visual-motor coordination is a key research field for understanding our brain and for developing new brain-like technologies.
To address the development and evaluation of bio-inspired control architectures based on cerebellar features, SP10 scientists and developers are collaborating in the implementation of several experiments in the Neurorobotics Platform.
Ismael Baira Ojeda from the Technical University of Denmark (DTU) visited the Scuola Superiore Sant’Anna (Pisa, Italy) to integrate the Adaptive Feedback Error Learning architecture  into the Neurorobotics Platform using the iCub humanoid robot. This control architecture uses a combination of Machine Learning techniques and cerebellar-like microcircuits in order to give an optimized input space , a fast learning and accuracy for the motor control of robots. In the experiment, the iCub was commanded to balance a ball towards the center of a board, which the iCub held in its hand.
Next, the AFEL architecture could be scaled up and combined with vision and motor control breakthroughs within the different SPs.
Thanks to all the scientists and developers for your support, especially Lorenzo Vannucci, Alessandro Ambrosano and Kenny Sharma!
 Tolu, S., Vanegas, M., Luque, N. R., Garrido, J. A., & Ros, E. (2012). Bio-inspired adaptive feedback error learning architecture for motor control. Biological Cybernetics, 1-16.
 Vijayakumar, S., D’souza, A., & Schaal, S. (2005). Incremental online learning in high dimensions. Neural Computation, 17(12), 2602-2634.