Low-latency Classification of Social Haptic Gestures Using Transformers

Abstract

Social touch, and its recognition and classification, is increasingly important in human-robot interaction. We present a Transformer-based model trained and evaluated on an open-source dataset. The dataset, the Human-Animal Affective Robot Touch (HAART) dataset, was collected for the 2015 Recognition of Touch Gesture Challenge (RTGC 2015) and contains different haptic actions directed at a robotic animal. The actions are recorded using a multi-resolution pressure sensor. We feed the output, containing the touch type to the Nao robot to make the robot sense the touch type. The proposed transformer-based gesture classification model achieved 72.8% classification accuracy in 2.67 seconds, which outperforms the best-submitted algorithm of the RTGC 2015 which has a test classification accuracy of 70.9 % and needed 8 seconds.