Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning

Omar Ibn Elkhatab Abdallah A. E. Zahra, David Navarro-Alarcon, Silvia Tolu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


The cerebellum plays a distinctive role within our motor control system to achieve fine and coordinated motions. While cerebellar lesions do not lead to a complete loss of motor functions, both action and perception are severally impacted. Hence, it is assumed that the cerebellum uses an internal forward model to provide anticipatory signals by learning from the error in sensory states. In some studies, it was demonstrated that the learning process relies on the jointspace error. However, this may not exist. This work proposes a novel fully spiking neural system that relies on a forward predictive learning by means of a cellular cerebellar model. The forward model is learnt thanks to the sensory feedback in task-space and it acts as a Smith predictor. The latter predicts sensory corrections in input to a differential mapping spiking neural network during a visual servoing task of a robot arm manipulator. In this paper, we promote the developed control system to achieve more accurate target reaching actions and reduce the motion execution time for the robotic reaching tasks thanks to the cerebellar predictive capabilities.
Original languageEnglish
Title of host publicationIEEE Int. Conf. on Robotics and Automation (ICRA)
Pageslink & page no. is not available yet
Publication statusPublished - May 2021
Event 2021 IEEE International Conference on Robotics and Automation - Xi'an, China
Duration: 30 May 20215 Jun 2021


Conference 2021 IEEE International Conference on Robotics and Automation
Abbreviated titleIEEE ICRA 2021
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