Emotion-Age-Gender-Nationality Based Intention Understanding in Human–Robot Interaction Using Two-Layer Fuzzy Support Vector Regression

An intention understanding model based on two-layer fuzzy support vector regression is proposed in human–robot interaction, where fuzzy c-means clustering is used to classify the input data, and intention understanding is mainly obtained by emotion, with identification information such as age, gender, and nationality. It aims to realize the transparent communication by understanding customers’ order intentions at a bar, in such a way that the social relationship between bar staffs and customers becomes smooth. To demonstrate the aptness of intention understanding model, experiments are designed in term of relationship between emotion-age-gender-nationality and order intention. Results show that the proposal obtains an intention understanding accuracy of 70 %/72 %/80 % with clusters number \(C= \) 2/3/6 (according to different genders/ages/nationalities), which is 23 %/26 %/33 % and 35.5 %/37.5 %/45.5 % higher than that of support vector regression (SVR) and back propagation neural networks (BPNN), respectively; the computational time of proposal is about 0.976 s/0.935 s/0.67 s with clusters number \(C=\) 2/3/6, while 1.889 s for SVR and 3.505 s for BPNN. Additionally, the preliminary application experiment is performed in the developing human–robot interaction system, called mascot robot system, where the experiment is performed in a scenario of “drinking at a bar”, result shows that the bar lady robot obtains an accuracy of 77.8 % for understanding customers’ order intentions and receives a satisfaction evaluation of “satisfied”. According to the preliminary application, the proposal is being extended to an ordering system in the bar for business communication.