March 10, 2020 Journal article Open Access

Albini Alessandro; Giorgio Cannata

DCAT Export

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        <foaf:name>Albini Alessandro</foaf:name>
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        <foaf:name>Giorgio Cannata</foaf:name>
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    <dct:title>Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body</dct:title>
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    <dcat:keyword>tactile sensing, robot skin, human robot interaction</dcat:keyword>
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    <dct:description>&lt;p&gt;This paper deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile&lt;br&gt; sensors covering its body. This problem is relevant in the domain of physical human-robot interaction for discriminating&lt;br&gt; between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a&lt;br&gt; safe interaction. The underlying assumption, used in this paper, is that voluntary physical interaction tasks involve hand&lt;br&gt; touch over the robot body, and therefore the capability of recognizing hand contacts is a key element to discriminate a&lt;br&gt; purposive human touch from other types of interaction.&lt;br&gt; The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements&lt;br&gt; associated to a non uniform cloud of 3D points (taxels) spread over a non linear manifold corresponding to the robot&lt;br&gt; body, into tactile images representing the contact pressure distribution in 2D. Tactile images can be processed using&lt;br&gt; deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various&lt;br&gt; hand segments: palm and single fingers.&lt;br&gt; Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed&lt;br&gt; methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis&lt;br&gt; concerning the transferability of the system has been performed, considering contacts occurring on a different&lt;br&gt; sensorized robot part.&lt;/p&gt;</dct:description>
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