Journal article Open Access

Pressure Distribution Classification and Segmentation of Human Hands in Contact with the Robot Body

Albini Alessandro; Giorgio Cannata


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    <subfield code="a">tactile sensing, robot skin, human robot interaction</subfield>
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    <subfield code="a">Albini Alessandro</subfield>
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    <subfield code="a">&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;</subfield>
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