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    Acknowledge of Emotions for Improving Student-Robot Interaction
    (TECH SCIENCE PRESS, 2023-05-25) Han, Hasan; Karadeniz, Oğuzcan; Dalyan, Tuğba; Sönmez, Elena Battini; Sarıoğlu, Baykal
    Robot companions will soon be part of our everyday life and students in the engineering faculty must be trained to design, build, and interact with them. The two affordable robots presented in this paper have been designed and constructed by two undergraduate students; one artifi-cial agent is based on the Nvidia Jetson Nano development board and the other one on a remote computer system. Moreover, the robots have been refined with an empathetic system, to make them more user-friendly. Since automatic facial expression recognition skills is a necessary pre-processing step for acknowledging emotions, this paper tested different variations of Convolutional Neural Networks (CNN) to detect the six facial expressions plus the neutral face. The state-of-the-art performance of 75.1% on the Facial Expression Recognition (FER) 2013 database has been reached by the ensemble voting method. The runner-up model is the Visual Geometry Group (VGG) 16 which has been adopted by the two robots to recognize the expressions of the human partner and behave accordingly. An empirical study run among 55 university students confirmed the hypothesis that contact with empathetic artificial agents contributes to increasing the acceptance rate of robots
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    EMRES: A New EMotional RESpondent Robot
    (IEEE-Inst Electrical Electronics Engineers Inc, 2022) Sonmez, Elena Battini; Han, Hasan; Karadeniz, Oguzcan; Dalyan, Tugba; Sarioglu, Baykal
    The aim of this work is to design an artificial empathetic system and to implement it into an EMotional RESpondent (EMRES) robot, called EMRES. Rather than mimic the expression detected in the human partner, the proposed system achieves a coherent and consistent emotional trajectory resulting in a more credible human-agent interaction. Inspired by developmental robotics theory, EMRES has an internal state and a mood, which contribute in the evolution of the flow of emotions; at every episode, the next emotional state of the agent is affected by its internal state, mood, current emotion, and the expression read in the human partner. As a result, EMRES does not imitate, but it synchronizes to the emotion expressed by the human companion. The agent has been trained to recognize expressive faces of the FER2013 database and it is capable of achieving 78.3% performance with wild images. Our first prototype has been implemented into a robot, which has been created for this purpose. An empirical study run with university students judged in a positive way the newly proposed artificial empathetic system.