A robust online touch pattern recognition for dynamic human-robot interaction

This paper presents a novel touch pattern recognition algorithm for dynamic proximate interaction between a robot and a human. At first, in order to guarantee reactive responses to various touch patterns, an online touch pattern algorithm is proposed based on a Temporal Decision Tree(TDT). Second, dynamic movements of a robot in a real interaction situation usually deteriorate the confidence level of the pattern classifier. A robust method to compensate for inconsistent recognition results in the dynamic interaction is proposed by a Consistency Index(CI), which estimates consistency degrees of human touch patterns over time. The algorithms are applied to a hard-cover touch recognition module, which is being developed for recognizing the four kinds of emotional touch patterns mainly used in human-robot affective interaction. The recognition performance is evaluated in a simple game scenario environment with KaMERo (KAIST Motion Expressive Robot), which is an emotionally interactive robot platform. The results show that the proposed algorithm guarantees commercially applicable recognition performance by compensating for the misclassification inherent in the dynamic movements of a robot.