Using Hebbian Learning for Training Spiking Neural Networks to Control Fingers of Robotic Hands

Abstract

Adaptability is one of the main characteristics of the bio-inspired control units for the anthropomorphic robotic hands. This characteristic provides the artificial hands with the ability to learn new motions and to improve the accuracy of the known ones. This paper presents a method to train spiking neural networks (SNNs) to control anthropomorphic fingers using proprioceptive sensors and Hebbian learning. Being inspired from physical guidance (PG), the proposed method eliminates the need for complex processing of the natural hand motions. To validate the proposed concept we implemented an electronic SNN that learns to control using the output of neuromorphic flexion and force sensors, two opposing actuated fingers actuated by shape memory alloys. Learning occurs when the untrained neural paths triggered by a command signal activate concurrently with the sensor specific neural paths that drive the motion detected by the flexion sensors. The results show that a SNN with a few neurons connects by synaptic potentiation the input neurons activated by the command signal to the output neurons which are activated during the passive finger motions. This mechanism is validated for grasping when the SNN is trained to flex simultaneously the index and thumb fingers if a push button is pressed. The proposed concept is suitable for implementing the neural control units of anthropomorphic robots which are able to learn motions by PG with proper sensors configuration.