TY - GEN
T1 - A Fully Spiking Neural Control System Based on Cerebellar Predictive Learning for Sensor-Guided Robots
AU - Zahra, Omar
AU - Navarro-Alarcon, David
AU - Tolu, Silvia
N1 - Funding Information:
This research work was supported in part by the Research Grants Council (grant 14203917), in part by the Jiangsu Industrial Technology Research Institute Collaborative Research Program Scheme (grant ZG9V), in part by the Key-Area Research and Development Program of Guangdong Province 2020 (project 76) and in part by PolyU (grants YBYT and ZZHJ).
Publisher Copyright:
© 2021 IEEE
PY - 2021/5
Y1 - 2021/5
N2 - 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 joint-space 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.
AB - 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 joint-space 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.
UR - http://www.scopus.com/inward/record.url?scp=85125488818&partnerID=8YFLogxK
U2 - 10.1109/ICRA48506.2021.9561127
DO - 10.1109/ICRA48506.2021.9561127
M3 - Conference article published in proceeding or book
AN - SCOPUS:85125488818
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 4423
EP - 4429
BT - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Y2 - 30 May 2021 through 5 June 2021
ER -