This paper proposes a computational model for selecting a suitable interlocutor of socially interactive robots in a situation interacting with multiple persons. To support this, a hybrid approach incorporating gaze control criteria and perceptual measurements for social cues is applied to the robot. For the perception part, representative non-verbal behaviors indicating human-interaction intent are designed based on the psychological analysis of human–human interaction, and these behavioral features are quantitatively measured by core perceptual components including visual, auditory, and spatial modalities. In addition, each aspect of recognition performance is improved through temporal confidence reasoning as a post-processing step. On the other hand, two factors of the physical space and conversational intimacy are tactically applied to the model calculation as a way of strengthening social gaze control effect of the robot. Interaction experiments with performance evaluation are given to verify that the proposed model is suitable to assess intended behaviors of individuals and perform gaze behavior about multiple persons. By showing a success rate of 93.3% in human decision-making criteria, it confirms a potential to establish socially acceptable gaze control in multiple-person interaction.

This work aims to propose an innovative mechanism of human–robot collaboration (HRC) for mobile service robots in the application of elderly and disabled assistance. Previous studies on HRC mechanism usually focused on integrating decision-making intelligence of human beings by qualitative judgment and reasoning intelligence of robots by quantitative calculation. Instead, novelties of the proposed methodology include (1) constructing an HRC framework by taking reference from the Adaptive Control of Thought – Rational (ACT-R) human cognitive architecture; (2) establishing semantic webs of cognitive reasoning through human–robot interaction (HRI) and HRC to plan and implement complex tasks; and (3) realizing human–robot intelligence fusion by mutual encouragement, connect, and integration of modules of human, robot, perception, HRI, and HRC in the ACT-R architecture. Its technical feasibility is validated by some selected experiments within a “pouring” scenario. Further, although this study is oriented to mobile service robots, the modularized design of hardware and software makes its extensive use feasible in other types of service robots like smart rehabilitation beds, wheelchairs, and cleaning equipments.