On the recognition of human hand touch from robotic skin pressure measurements using convolutional neural networks

This paper presents a novel approach for recognizing a human hand touch by processing pressure measurements generated by a robotic skin. Physical cooperation among humans is mainly based on the sense of touch and usually starts with hand contacts. If a robot can distinguish a human touch from a generic contact, the human-robot cooperation can be more natural and effective. The proposed approach consists in transforming the sensor pressure measurements distributed on the robot surface into a convenient 2D representation of the contact shape, i.e., a contact image. The image-based representation of contacts allows facing the problem of human touch classification by applying machine learning methods already developed for image classification. The experiments have been performed using a robotic skin, composed of 768 tactile elements, placed on a Baxter robot forearm. The contact classification has been performed using a Convolutional Neural Network obtaining an accuracy higher than 97% experimentally validating the proposed approach.