Ken E. Friedl
Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch
Friedl, Ken E.; Voelker, Aaron R.; Friedl, Ken; Voelker, Aaron; Peer, Angelika; Eliasmith, Chris
Authors
Aaron R. Voelker
Ken Friedl
Aaron Voelker
Angelika Peer
Chris Eliasmith
Abstract
© 2016 IEEE. Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online.
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 10, 2016 |
Online Publication Date | Jan 12, 2016 |
Publication Date | Jan 1, 2016 |
Publicly Available Date | Jun 7, 2019 |
Journal | IEEE Robotics and Automation Letters |
Print ISSN | 2377-3766 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 1 |
Issue | 1 |
Pages | 516-523 |
DOI | https://doi.org/10.1109/LRA.2016.2517213 |
Keywords | neurorobotics, biologically-inspired robots, force and tactile sensing |
Public URL | https://uwe-repository.worktribe.com/output/914700 |
Publisher URL | http://dx.doi.org/10.1109/LRA.2016.2517213 |
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