Detecting and Classifying Human Touches in a Social Robot Through Acoustic Sensing and Machine Learning

An important aspect in Human-Robot Interaction is responding to different kinds of touch stimuli. To date, several technologies have been explored to determine how a touch is perceived by a social robot, usually placing a large number of sensors throughout the robot's shell. In this work, we introduce a novel approach, where the audio acquired from contact microphones located in the robot's shell is processed using machine learning techniques to distinguish between different types of touches. The system is able to determine when the robot is touched (touch detection), and to ascertain the kind of touch performed among a set of possibilities: stroke, tap, slap, and tickle (touch classification). This proposal is cost-effective since just a few microphones are able to cover the whole robot's shell since a single microphone is enough to cover each solid part of the robot. Besides, it is easy to install and configure as it just requires a contact surface to attach the microphone to the robot's shell and plug it into the robot's computer. Results show the high accuracy scores in touch gesture recognition. The testing phase revealed that Logistic Model Trees achieved the best performance, with an F-score of 0.81. The dataset was built with information from 25 participants performing a total of 1981 touch gestures.