University of Hertfordshire

By the same authors

Histogram based classification of tactile patterns on periodically distributed skin sensors for a humanoid robot

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

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Original languageEnglish
Title of host publicationProcs IEEE International Workshop on Robot and Human Interactive Communication
Subtitle of host publicationRO-MAN 2011
PublisherIEEE
Pages433-440
Number of pages8
ISBN (Electronic)978-1-4577-1572-3
ISBN (Print)978-1-4577-1571-6
DOIs
Publication statusPublished - 2011
EventRO-MAN 2011 - Atlanta, United States
Duration: 31 Jul 20113 Aug 2011

Conference

ConferenceRO-MAN 2011
CountryUnited States
CityAtlanta
Period31/07/113/08/11

Abstract

The main target of this work is to improve human-robot interaction capabilities, by adding a new modality of sense, touch, to KASPAR, a humanoid robot. Large scale distributed skin-like sensors are designed and integrated on the robot, covering KASPAR at various locations. One of the challenges is to classify different types of touch. Unlike digital images represented by grids of pixels, the geometrical structure of the sensor array limits the capability of straightforward application of well-established approaches for image patterns. This paper introduces a novel histogram-based classification algorithm, transforming tactile data into histograms of local features termed as codebook. Tactile pattern can be invariant at periodical locations, allowing tactile pattern classification using a smaller number of training data, instead of using training data from everywhere on the large scale skin sensors. To generate the codebook, this method uses a two-layer approach, namely local neighbourhood structures and encodings of pressure distribution of the local neighbourhood. Classification is performed based on the constructed features using Support Vector Machine (SVM) with the intersection kernel. Real experimental data are used for experiment to classify different patterns and have shown promising accuracy. To evaluate the performance, it is also compared with the SVM using the Radial Basis Function (RBF) kernel and results are discussed from both aspects of accuracy and the location invariance property.

ID: 457686