Abstract
Research suggests that emotionally responsive machines that can simulate empathy increase de acceptance of users towards them, as the feeling of affinity towards the machine reduces negative perceptual feedback. In order to endow a robot with emotional intelligence, it must be equipped with sensors capable of capturing users’ emotions (sense), appraisal captured emotions to regulate its internal state (compute), and finally perform tasks where actions are regulated by the computed “emotional” state (act). However, despite the impressive progress made in recent years in terms of artificial intelligence, speech recognition and synthesis, computer vision and many other disciplines directly and indirectly related to artificial emotional recognition and behavior, we are still far from being able to endow robots with the empathic capabilities of a human being. This article aims to give an overview of the implications of introducing emotional intelligence in robotic constructions by discussing recent advances in emotional intelligence in robotics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aranha, R.V., Corrêa, C.G., Nunes, F.L.S.: Adapting software with affective computing: a systematic review. IEEE Trans. Affect. Comput. 1 (2019). https://doi.org/10.1109/TAFFC.2019.2902379
Yadegaridehkordi, E., Noor, N.F.B.M., Ayub, M.N.B., Affal, H.B., Hussin, N.B.: Affective computing in education: a systematic review and future research. Comput. Educ. 142, 103649 (2019). https://doi.org/10.1016/j.compedu.2019.103649
Dudek, M., Baisch, S., Knopf, M., Kolling, T.: This isn’t me!: the role of age-related self- and user images for robot acceptance by elders. Int. J. Soc. Robot. (2020). https://doi.org/10.1007/s12369-020-00678-1
Mele, C., et al.: Understanding robot acceptance/rejection: the SAR model. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 470–475 (2020). https://doi.org/10.1109/RO-MAN47096.2020.9223577
Meissner, A., Trübswetter, A., Conti-Kufner, A.S., Schmidtler, J.: Friend or foe? Understanding assembly workers 2019; acceptance of human-robot collaboration. ACM Trans. Hum.-Robot Interacti. 10(1), 3:1–3:30. https://doi.org/10.1145/3399433
Savery, R., Weinberg, G.: A survey of robotics and emotion: classifications and models of emotional interaction. In: 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), pp. 986–993 (2020). https://doi.org/10.1109/RO-MAN47096.2020.9223536
Salovey, P., Mayer, J.D.: Emotional intelligence. Imagin. Cogn. Pers. 9(3), 185–211 (1990). https://doi.org/10.2190/DUGG-P24E-52WK-6CDG
What is a robot? - ROBOTS: your guide to the world of robotics. (n.d.). https://robots.ieee.org/learn/what-is-a-robot/. Accessed: 28 Apr 2021
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980). https://doi.org/10.1037/h0077714
Shu, L., et al.: A review of emotion recognition using physiological signals. Sensors 18(7), 2074 (2018). https://doi.org/10.3390/s18072074
van der Kruk, E., Reijne, M.M.: Accuracy of human motion capture systems for sport applications; state-of-the-art review. Eur. J. Sport Sci. 18(6), 806–819 (2018). https://doi.org/10.1080/17461391.2018.1463397
Rohlfing, M.L., Buckley, D.P., Piraquive, J., Stepp, C.E., Tracy, L.F.: Hey Siri: How effective are common voice recognition systems at recognizing dysphonic voices? The Laryngoscope (2021). https://doi.org/10.1002/lary.29082
Samadiani, N., et al.: A review on automatic facial expression recognition systems assisted by multimodal sensor data. Sensors 19(8), 1863 (2019). https://doi.org/10.3390/s19081863
Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T.: Speech emotion recognition using deep learning techniques: a review. IEEE Access 7, 117327–117345 (2019). https://doi.org/10.1109/ACCESS.2019.2936124
Abdullah, S.M.S.A., Ameen, S.Y.A., Sadeeq, M.A.M., Zeebaree, S.: Multimodal emotion recognition using deep learning. J. Appl. Sci. Technol. Trends 2(2), 52–58 (2021). https://doi.org/10.38094/jastt20291
Tzirakis, P., Chen, J., Zafeiriou, S., Schuller, B.: End-to-end multimodal affect recognition in real-world environments. Inf. Fusion 68, 46–53 (2021). https://doi.org/10.1016/j.inffus.2020.10.011
Li, S., et al.: Bi-modality fusion for emotion recognition in the wild. In: 2019 International Conference on Multimodal Interaction, pp. 589–594 (2019). https://doi.org/10.1145/3340555.3355719
Moerland, T.M., Broekens, J., Jonker, C.M.: Emotion in reinforcement learning agents and robots: a survey. Mach. Learn. 107(2), 443–480 (2017). https://doi.org/10.1007/s10994-017-5666-0
Zhou, Q.: Multi-layer affective computing model based on emotional psychology. Electron. Commer. Res. 18(1), 109–124 (2017). https://doi.org/10.1007/s10660-017-9265-8
Calvo, R.: The Oxford Handbook of Affective Computing. Oxford University Press (2015). https://doi.org/10.1093/oxfordhb/9780199942237.001.0001
Broekens, J., Bosse, T., Marsella, S.C.: Challenges in computational modeling of affective processes. IEEE Trans. Affect. Comput. 4(3), 242–245 (2013). https://doi.org/10.1109/T-AFFC.2013.23
Taverner, J., Vivancos, E., Botti, V.: A fuzzy appraisal model for affective agents adapted to cultural environments using the pleasure and arousal dimensions. Inf. Sci. 546, 74–86 (2021). https://doi.org/10.1016/j.ins.2020.08.006
Ashwin, T.S., Guddeti, R.M.R.: Automatic detection of students’ affective states in classroom environment using hybrid convolutional neural networks. Educ. Inf. Technol. 25(2), 1387–1415 (2019). https://doi.org/10.1007/s10639-019-10004-6
Bird, J.J., Ekárt, A., Faria, D.R.: Chatbot interaction with artificial intelligence: human data augmentation with T5 and language transformer ensemble for text classification. ArXiv: 2010.05990 [Cs] (2020). http://arxiv.org/abs/2010.05990
Fiorini, L., Mancioppi, G., Semeraro, F., Fujita, H., Cavallo, F.: Unsupervised emotional state classification through physiological parameters for social robotics applications. Knowl.-Based Syst. 190, 105217 (2020). https://doi.org/10.1016/j.knosys.2019.105217
Ivanovic, M., et al.: Emotional agents—state of the art and applications. Comput. Sci. Inf. Syst. (2015). https://doi.org/10.2298/CSIS141026047I
Newell, A.: SOAR as a unified theory of cognition: issues and explanations. Behav. Brain Sci. 15(3), 464–492 (1992). https://doi.org/10.1017/S0140525X00069740
Zuo, G., Pan, T., Zhang, T., Yang, Y.: SOAR improved artificial neural network for multistep decision-making tasks. Cogn. Comput. 13(3), 612–625 (2020). https://doi.org/10.1007/s12559-020-09716-6
Schindler, S., Bublatzky, F.: Attention and emotion: an integrative review of emotional face processing as a function of attention. Cortex 130, 362–386 (2020). https://doi.org/10.1016/j.cortex.2020.06.010
Marcos, S., Gómez-García-Bermejo, J., Zalama, E.: A realistic, virtual head for human–computer interaction. Interact. Comput. 22(3), 176–192 (2010). https://doi.org/10.1016/j.intcom.2009.12.002
Fernandez, R., John, N., Kirmani, S., Hart, J., Sinapov, J., Stone, P.: Passive demonstrations of light-based robot signals for improved human interpretability. In: 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 234–239. https://doi.org/10.1109/ROMAN.2018.8525728
MacDorman, K.F., Ishiguro, H.: The uncanny advantage of using androids in cognitive and social science research. Interact. Stud.: Soc. Behav. Commun. Biol. Artif. Syst. 7(3), 297–337 (2006). https://doi.org/10.1075/is.7.3.03mac
Matthews, G., et al.: Evolution and revolution: personality research for the coming world of robots, artificial intelligence, and autonomous systems. Pers. Individ. Differ. 169, 109969 (2021). https://doi.org/10.1016/j.paid.2020.109969
Korn, O., Akalin, N., Gouveia, R.: Understanding cultural preferences for social robots: a study in German and Arab communities. ACM Trans. Hum.-Robot Interact. 10(2), 12:1–12:19 (2021). https://doi.org/10.1145/3439717
Nishio, T., Yoshikawa, Y., Ogawa, K., Ishiguro, H.: Development of an effective information media using two android robots. Appl. Sci. 9(17), 3442 (2019). https://doi.org/10.3390/app9173442
Doering, M., Glas, D.F., Ishiguro, H.: Modeling interaction structure for robot imitation learning of human social behavior. IEEE Trans. Hum.-Mach. Syst. 49(3), 219–231 (2019). https://doi.org/10.1109/THMS.2019.2895753
Pablos, S.M., García-Bermejo, J.G., Zalama Casanova, E., López, J.: Dynamic facial emotion recognition oriented to HCI applications. Interact. Comput. 27(2), 99–119 (2015). https://doi.org/10.1093/iwc/iwt057
Strathearn, C., Ma, M.: Modelling user preference for embodied artificial intelligence and appearance in realistic humanoid robots. Informatics 7(3), 28 (2020). https://doi.org/10.3390/informatics7030028
Cañamero, L.: Emotion understanding from the perspective of autonomous robots research. Neural Netw. 18(4), 445–455 (2005). https://doi.org/10.1016/j.neunet.2005.03.003
Dautenhahn, K., Woods, S., Kaouri, C., Walters, M. L., Koay, K.L., Werry, I.: What is a robot companion—Friend, assistant or butler? In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1192–1197 (2005). https://doi.org/10.1109/IROS.2005.1545189
Whittaker, S., Rogers, Y., Petrovskaya, E., Zhuang, H.: Designing Personas for expressive robots: personality in the new breed of moving, speaking, and colorful social home robots. ACM Trans. Hum.-Robot Interact. 10(1), 8:1–8:25 (2021). https://doi.org/10.1145/3424153
Schoenherr, J.R., Burleigh, T.J.: Uncanny sociocultural categories. Front. Psychol. 5. (2015). https://doi.org/10.3389/fpsyg.2014.01456
Cheetham, M.: Editorial: the uncanny valley hypothesis and beyond. Front. Psychol. 8 (2017). https://doi.org/10.3389/fpsyg.2017.01738
Gee, F.C., Browne, W.N., Kawamura, K.: Uncanny valley revisited. In: ROMAN 2005. IEEE International Workshop on Robot and Human Interactive Communication, pp. 151–157 (2005). https://doi.org/10.1109/ROMAN.2005.1513772
Brink, K.A., Gray, K., Wellman, H.M.: Creepiness creeps In: uncanny valley feelings are acquired in childhood. Child Dev. 90(4), 1202–1214. (2019). https://doi.org/10.1111/cdev.12999
Feng, S., et al.: The uncanny valley effect in typically developing children and its absence in children with autism spectrum disorders. PLoS One 13(11), e0206343 (2018). https://doi.org/10.1371/journal.pone.0206343
Tinwell, A., Sloan, R.J.S.: Children’s perception of uncanny human-like virtual characters. Comput. Hum. Behav. 36, 286–296 (2014). https://doi.org/10.1016/j.chb.2014.03.073
Acknowledgments
This research was partially funded by the Spanish Government Ministry of Economy and Competitiveness through the DEFINES project grant number (TIN2016-80172-R) and the Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Marcos-Pablos, S., García-Peñalvo, F.J. (2022). Emotional Intelligence in Robotics: A Scoping Review. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-87687-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87686-9
Online ISBN: 978-3-030-87687-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)