Map acquisition and classification of haptic interaction using cross correlation between distributed tactile sensors on the whole body surface

The distance between robots and users increasingly shrinks and with it the potential for robots to inadvertently harm users increasingly grows. Robots need to detect ongoing haptic interaction with users via malleable skin with flexible tactile sensors covering the whole body surface in order to reduce the risk and to realize enriched interactions. In this paper, we propose a feature space based on cross-correlation between tactile sensors, and we visualized it as a 2-dimensional map which we call Somatosensory Map. We construct a haptic interaction database using scenario based haptic interaction between humans and a robot that we equipped with distributed tactile sensors covering the whole body. In the Somatosensory Map, highly correlated sensors make clusters corresponding to distinctive haptic interactions between the robot and the subject. Each haptic interaction can thus be classified using cross-correlation between tactile sensors. To confirm the validity of our proposal, we apply the K-Nearest Neighbor (KNN) method using the feature space. As a validation test result, despite KNN being one of the simplest methods, over 60% of haptic interaction in which we may expect subject’s touches can be recognized. In conclusion, the cross-correlation includes good features to classify the interaction. As future work, the classifier should be improved to use effective cross-correlation shown as distribution of distinctive clusters in the Somatosensory Map.