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

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