The Methodology of Quantitative Social Intention Evaluation and Robot Gaze Behavior Control in Multiobjects Scenario

This article focuses on the multiple objects selection problem for the robot in social scenarios, and proposes a novel methodology composed of quantitative social intention evaluation and gaze behavior control. For the social scenarios containing various persons and multimodal social cues, a combination of the entropy weight method (EWM) and gray correlation-order preference by similarity to the ideal solution (GC-TOPSIS) model is proposed to fuse the multimodal social cues, and evaluate the social intention of candidates. According to the quantitative evaluation of social intention, a robot can generate the interaction priority among multiple social candidates. To ensure this interaction selection mechanism in behavior level, an optimal control framework composed of model predictive controller (MPC) and online Gaussian process (GP) observer is employed to drive the eye-head coordinated gaze behavior of robot. Through the experiments conducted on the Xiaopang robot, the availability of the proposed methodology can be illustrated. This work enables robots to generate social behavior based on quantitative intention perception, which could bring the potential to explore the sensory principles and biomechanical mechanism underlying the human-robot interaction, and broaden the application of robot in the social scenario.