Social Robotics and Human Emotional Responses

  • Authors

    • Dr. Matteo Rossi Professor, Sapienza University of Rome, Italy. Author
    • Dr. Giulia Romano Associate Professor, University of Milan, Italy. Author

    DOI:

    https://doi.org/10.67228/3071561X/IJIRHT-2018PII4J9V

    Published 10-03-2018

  • Social Robotics, Human–Robot Interaction, Emotional Computing, Affective Response, Artificial Intelligence, Machine Learning, User Experience

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    M. Rossi and G. Romano, “Social Robotics and Human Emotional Responses”, IJIRHT, vol. 1, no. 2, pp. 01–15, Oct. 2018, doi: 10.67228/3071561X/IJIRHT-2018PII4J9V.

  • Abstract

    Social robotics has come to be a cross-disciplinary study involving artificial intelligence, psychology, cognitive science, and human-computer interaction to create robots that can communicate with humans in a natural manner. The paper explores the interconnection between social robots and human emotional reactions, particularly, considering how the aesthetics of robots, their behavior, types of communication, and level of awareness to the surrounding environment affect the engagement of emotions. The aim of the study is the examination of emotional responses caused by social robots to structured and semi-structured setting, such as educational, healthcare, and home settings. Mixed method research design, which incorporated experimental trials, surveys which were done through questionnaires and analysis of behavior was used. A group of participants was exposed to a humanoid and a non-humanoid robot in controlled conditions and their emotional reactions were assessed by means of standardized affect scales, facial expression scale and physiological measures. The offered methodology will combine emotion recognition by machine learning and statistical validation to make it reliable and reproducible. Findings indicate that socially adaptive robots are much better in managing user trust, comfort and interest than the ones with static-interaction. Moreover, it was even discovered that emotional congruence between the reaction of robots and the affective states of people was a significant factor in long-term acceptance. The presented paper is an addition to the current body of knowledge, suggesting a synthetic evaluation framework and providing the empiric evidence on the emotion dynamics of a human-robot interaction. It is anticipated that the results would aid in creating such emotionally intelligent robots to be employed in the real world and serve as a guide to further studies in affective computing and social robotics.

  • References

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    [2] Affective Computing – Picard, R. W. (1997). Affective Computing. MIT Press.

    [3] The Cognitive Structure of Emotions – Ortony, A., Clore, G. L., & Collins, A. (1988). The Cognitive Structure of Emotions. Cambridge University Press.

    [4] Handbook of Affective Sciences – Davidson, R. J., Goldsmith, H. H., & Scherer, K. R. (2003). Handbook of Affective Sciences. Oxford University Press.

    [5] Social Robotics – Mutlu, B., et al. (2016). Social Robotics. Springer.

    [6] Deep Learning – Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

    [7] Emotional Design – Norman, D. A. (2004). Emotional Design. Basic Books.

    [8] Affective Neuroscience – Panksepp, J. (1998). Affective Neuroscience. Oxford University Press.

    [9] Robots That Talk and Listen – Markowitz, J. A. (2015). Robots That Talk and Listen. Morgan & Claypool.

    [10] Pattern Recognition and Machine Learning – Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

    [11] Socially Intelligent Agents – Dautenhahn, K., et al. (2002). Socially Intelligent Agents. Springer.

    [12] Emotion-Oriented Systems – Petta, P. (2001). Emotion-Oriented Systems. Springer.

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