Object Contact Shape Classification Using Neuromorphic Spiking Neural Network with STDP Learning
- Resource Type
- Conference
- Authors
- Dabbous, Ali; Ibrahim, Ali; Alameh, Mohamad; Valle, Maurizio; Bartolozzi, Chiara
- Source
- 2022 IEEE International Symposium on Circuits and Systems (ISCAS) Circuits and Systems (ISCAS), 2022 IEEE International Symposium on. :1052-1056 May, 2022
- Subject
- Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Shape
Neurons
Tactile sensors
Hardware
Software
Real-time systems
Sensor systems
Tactile perception
intelligent tactile sensors
Shape classification
event driven sensing
neuromorphic
spiking neural network
- Language
- ISSN
- 2158-1525
Tactile object shapes are considered as important properties in robotic manipulation. Many researches have focused recently on using tactile sensing systems to enable tactile information processing in robotics. Spiking Neural Networks (SNNs) are emerging as promising methods alternative to deep learning due to their ability to process information in an event-driven manner. In this paper, we propose a SNN architecture and hardware implementation for tactile object shapes recognition. The network is fed by an array of 160 piezoresistive tactile sensors where the object shapes are applied. Results demonstrate that the proposed system is able to discriminate the tactile object shapes with 100% accuracy on unseen data having time steps up to 0.1 ms. Moreover, the network has been implemented on a Raspberry Pi platform achieving real time classification.