Sociability Modelling in Robot Motion for Generating Socially Predictable Trajectories

Modelling and quantifying human socialness onto robots remain a challenge, due to the complex mechanisms and reasoning processes that incorporate human intelligence to enable social behaviours. In this paper, we propose a novel approach of modelling human sociability in the social context of human–robot interactions by deriving the sociability score, which integrates both legible and trustable motion. To generate socially accepted motions, with potential deployment in real-time and dynamic environments, a new procedure is developed to encode human trustability onto robot motion, with the introduction of the trustability score, which explores perceived benevolence and the importance of initial trust. By applying the trust region of predictability, trustably and socially predictable trajectories are thus generated that can be identified and interpreted by humans consistently as verified by experiments. The experimental results also demonstrated that the scores computed by the proposed method can effectively capture their respective defined characteristics. Furthermore, to generate and evaluate predictable trajectories independent of other trajectories, a modified predictability score computation has been derived. Finally, as a step towards creating social intelligence, we train a deep learning-based classifier to identify socially predictable trajectories, mimicking humans’ ability to recognise such motion.