Touch Gesture Recognition for Service Robots

In order to realize tactile perception of robots in the process of human-robot interaction, a touch gesture recognition method for service robots is proposed. Firstly, the electronic skin is installed on the service robot, and an affective gesture dataset is built by collecting 10 kinds of touch gesture signals from 15 subjects. Then, a factorized spatio-temporal convolutional neural network ((2+1)D CNN) is used to classify the gestures applied to the service robot. The results show that the gesture recognition accuracies within-subject and across-subject are 90.25% and 83.44%, respectively. By changing the adjustment factors of spatio-temporal channels in the model, the model parameters can be greatly reduced while the recognition accuracy decreases slightly. Based on the touch gesture recognition experiment using electronic skin, it is preliminarily believed that the use of (2+1)D CNN can achieve human touch gesture recognition with higher accuracy and lower computational cost, which can realize the emotional interaction between service robots and humans through electronic skin.