The study of attention estimation for child-robot interaction scenarios

Abstract

One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.

A simulated risk assessment of human-robot interaction in the domestic environment

“…The programming by demonstration system was presented by Amar et al [20] on a trajectory level to reproduce hand/tool movement with a robot manipulator, which was achieved with the ArToolkit, tracking user movement and the trajectories with the limited cubic spine reworked. Attamimi et al [21] provided in a free play scenario a way to estimate the concentration of an infant, one of the most critical mental states. Interaction between child and robot (CRI).…”