Continuous material identification through tactile sensing

A. Gómez Eguíluz, I. Rañó, S.A. Coleman, T.M. McGinnity

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Tactile sensing has recently attracted significant research interest in robotics. Despite the fact that tactile sensors provide temporal sequences of readings, state-of-the-art material recognition approaches are episodic, i.e. a whole sequence of readings is processed to identify the material. Based on vibration frequency response, this work presents an online identification technique using recursive estimation of the probability of identifying a set of materials, i.e. casting the classification problem as a hidden Markov model (HMM) state estimation problem. This allows for faster identification of most materials and does not require several exploratory movements. Our results show that when enough evidence is gathered, the system eventually achieves perfect recognition of our experimental set of 34 materials with an average identification time of ≤ 0.5 seconds. To prove the accuracy of this method, we also conducted a comparative experiment with commonly used machine learning algorithms for material identification such as k-Nearest Neighbour(KNN), an Artificial Neural Network(ANN) and Support Vector Machine(SVM).
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
Publication date2016
ISBN (Print)9781509006212
ISBN (Electronic)9781509006205
Publication statusPublished - 2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks - Vancouver, Canada
Duration: 24. Jul 201629. Jul 2016


Conference2016 International Joint Conference on Neural Networks


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