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- Title
Simultaneous Velocity and Texture Classification from a Neuromorphic Tactile Sensor Using Spiking Neural Networks.
- Authors
Brayshaw, George; Ward-Cherrier, Benjamin; Pearson, Martin J.
- Abstract
The neuroTac, a neuromorphic visuo-tactile sensor that leverages the high temporal resolution of event-based cameras, is ideally suited to applications in robotic manipulators and prosthetic devices. In this paper, we pair the neuroTac with Spiking Neural Networks (SNNs) to achieve a movement-invariant neuromorphic tactile sensing method for robust texture classification. Alongside this, we demonstrate the ability of this approach to extract movement profiles from purely tactile data. Our systems achieve accuracies of 95% and 83% across their respective tasks (texture and movement classification). We then seek to reduce the size and spiking activity of our networks with the aim of deployment to edge neuromorphic hardware. This multi-objective optimisation investigation using Pareto frontiers highlights several design trade-offs, where high activity and large network sizes can both be reduced by up to 68% and 94% at the cost of slight decreases in accuracy (8%).
- Subjects
ARTIFICIAL neural networks; TACTILE sensors; PROSTHETICS; CLASSIFICATION; VELOCITY; ARTIFICIAL skin; MULTISPECTRAL imaging
- Publication
Electronics (2079-9292), 2024, Vol 13, Issue 11, p2159
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics13112159