Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actuators made of shape memory alloy and receives feedback from neuromorphic sensors that convert the joint rotation angle and compression force into the spiking frequency. The adaptive SNN activates independent neural paths that correspond to angle intervals and learns in which of these intervals the rotation the finger rotation is stopped by an external force. Learning occurs when angle-specific neural paths are stimulated concurrently with the supraliminar stimulus that activates all the neurons that inhibit the SNN output stopping the finger. The results showed that after learning, the finger stopped in the angle interval in which the angle-specific neural path was active, without the activation of the supraliminar stimulus. The proposed concept can be used to implement control units for anthropomorphic robots that are able to learn motions unsupervised, based on principles of high biological plausibility.
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.
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