Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Review on HRI for Child Learning

Author : Shubham Morya 1 P. Raja 2

Date of Publication :3rd June 2022

Abstract: Human robot interaction (HRI) becomes an effective method of interacting with kids. Many HRI's system are currently available. In this paper, a review of various robotic tutor system based on design aspects, safety aspects, mechanical and electronic aspects of robots, learning outcome of kids, type of interaction, configuration of various learning modules, real time behavior monitoring and socio centric aspect are presented in this paper. As the robot tutoring has several benefits over human tutoring because these kinds of system have various pre-installed learning modules so that without any human monitoring child learn its concept. The robotic system is now a days become very effective for the learner for real time applications

Reference :

    1. E. A. Konijn and J. F. Hoorn, "Robot tutor and pupils’ educational ability: Teaching the times tables," Computers & Education, vol. 157, 2020, doi: 10.1016/j.compedu.2020.103970.
    2. O. Engwall and J. Lopes, "Interaction and collaboration in robot-assisted language learning for adults," Computer Assisted Language Learning, pp. 1-37, 2020, doi: 10.1080/09588221.2020.1799821.
    3. "TNS Opinion & Social, Public Attitudes towards Robots, 2012."
    4. M. Chassignol, A. Khoroshavin, A. Klimova, and A. J. P. C. S. Bilyatdinova, "Artificial Intelligence trends in education: a narrative overview," vol. 136, pp. 16-24, 2018.
    5. D. Mourtzis, E. Vlachou, G. Dimitrakopoulos, and V. J. P. m. Zogopoulos, "Cyber-physical systems and education 4.0–the teaching factory 4.0 concept," vol. 23, pp. 129-134, 2018.
    6. J. Kim, K. Merrill, K. Xu, and D. D. Sellnow, "My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education," International Journal of Human–Computer Interaction, vol. 36, no. 20, pp. 1902-1911, 2020, doi: 10.1080/10447318.2020.1801227.
    7. S. Apostel et al., Transformative Student Experiences in Higher Education: Meeting the Needs of the Twenty-First Century Student and Modern Workplace. Rowman & Littlefield, 2018.
    8. H. S. J. A. I. R. Nwana, "Intelligent tutoring systems: an overview," vol. 4, no. 4, pp. 251-277, 1990.
    9. D. Leyzberg, E. Avrunin, J. Liu, and B. Scassellati, "Robots that express emotion elicit better human teaching," in 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2011: IEEE, pp. 347-354.
    10. J. Fasola and M. J. J. J. o. H.-R. I. Matarić, "A socially assistive robot exercise coach for the elderly," vol. 2, no. 2, pp. 3-32, 2013.
    11. T. Belpaeme, J. Kennedy, A. Ramachandran, B. Scassellati, and F. Tanaka, "Social robots for education: A review," Sci Robot, vol. 3, no. 21, Aug 15 2018, doi: 10.1126/scirobotics.aat5954.
    12.  J. H. Yousif, M. Al-Hosini, S. Al-Sheyadi, A. Al-Ofui, M. J. I. J. o. C. Al-Sheyadi, and A. Sciences, "Questionnaire of Using Humanoid Robot for Teaching and Learning Kids," vol. 4, no. 2, pp. 324-329, 2018.
    13. A. Ramachandran, C.-M. Huang, E. Gartland, and B. Scassellati, "Thinking Aloud with a Tutoring Robot to Enhance Learning," presented at the Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018.
    14.  T. Adamson et al., "Why We Should Build Robots That Both Teach and Learn," presented at the Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot
    15. L.-E. I. Cuadrado, Á. M. Riesco, and F. de la Paz López, "FER in Primary School Children for Affective Robot Tutors," in International Work-Conference on the Interplay Between Natural and Artificial Computation, 2019: Springer, pp. 461-471.
    16.  L. Clunne-Kiely et al., "Modelling and implementation of humanoid robot behaviour," vol. 112, pp. 2249-2258, 2017.
    17. Z. Shi, T. R. Groechel, S. Jain, K. Chima, and M. J. J. a. p. a. Matarić, "Toward Personalized Affect-Aware Socially Assistive Robot Tutors in Long-Term Interventions for Children with Autism," 2021.
    18. D. Leyzberg, S. Spaulding, and B. Scassellati, "Personalizing robot tutors to individuals’ learning differences," in 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2014: IEEE, pp. 423-430.
    19. G. Gordon et al., "Affective personalization of a social robot tutor for children’s second language skills," in Proceedings of the AAAI conference on artificial intelligence, 2016, vol. 30, no. 1.
    20. L. Raggioli and S. Rossi, "A reinforcement-learning approach for adaptive and comfortable assistive robot monitoring behavior," in 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2019: IEEE, pp. 1-6.
    21. S. Rossi, E. Leone, and M. Staffa, "Using random forests for the estimation of multiple users’ visual focus of attention from head pose," in Conference of the Italian Association for Artificial Intelligence, 2016: Springer, pp. 89-102.
    22. M. Sorostinean and A. Tapus, "Activity recognition based on RGB-D and thermal sensors for socially assistive robots," in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018: IEEE, pp. 1298-1304.
    23.  J. J. Lee, F. Sha, and C. Breazeal, "A Bayesian theory of mind approach to nonverbal communication," in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019: IEEE, pp. 487-496.
    24. M. Oudah, V. Babushkin, T. Chenlinangjia, and J. W. Crandall, "Learning to interact with a human partner," in 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2015: IEEE, pp. 311-318.
    25. A. Nanavati, M. Doering, D. Brščić, and T. Kanda, "Autonomously learning one-to-many social interaction logic from human-human interaction data," in Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020, pp. 419-427.
    26. D. Hood, S. Lemaignan, and P. Dillenbourg, "When children teach a robot to write: An autonomous teachable humanoid which uses simulated handwriting," in Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, 2015, pp. 83-90.
    27. C. Moro, G. Nejat, and A. J. A. T. o. H.-R. I. Mihailidis, "Learning and personalizing socially assistive robot behaviors to aid with activities of daily living," vol. 7, no. 2, pp. 1-25, 2018.
    28. A. Bajcsy, D. P. Losey, M. K. O'Malley, and A. D. Dragan, "Learning from physical human corrections, one feature at a time," in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, 2018, pp. 141-149.
    29.  M. Tykal, A. Montebelli, and V. Kyrki, "Incrementally assisted kinesthetic teaching for programming by demonstration," in 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016: IEEE, pp. 205-212.
    30.  A. Y. Gao, W. Barendregt, and G. Castellano, "Personalised human-robot co-adaptation in instructional settings using reinforcement learning," in IVA Workshop on Persuasive Embodied Agents for Behavior Change: PEACH 2017, August 27, Stockholm, Sweden, 2017.
    31. H. W. Park, I. Grover, S. Spaulding, L. Gomez, and C. Breazeal, "A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education," in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, no. 01, pp. 687-694.
    32. P. Sequeira et al., "Discovering social interaction strategies for robots from restricted-perception Wizard-of-Oz studies," in 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016: IEEE, pp. 197-204.
    33. A. M. Rosenthal-von der Pütten and J. J. I. J. o. S. R. Hoefinghoff, "The more the merrier? Effects of humanlike learning abilities on humans’ perception and evaluation of a robot," vol. 10, no. 4, pp. 455-472, 2018.
    34. U. Ehsan, P. Tambwekar, L. Chan, B. Harrison, and M. O. Riedl, "Automated rationale generation: a technique for explainable AI and its effects on human perceptions," in Proceedings of the 24th International Conference on Intelligent User Interfaces, 2019, pp. 263-274.
    35. B. Hayes and J. A. Shah, "Improving robot controller transparency through autonomous policy explanation," in 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI, 2017: IEEE, pp. 303-312.
    36.  S. Shiotani, T. Tomonaka, K. Kemmotsu, S. Asano, K. Oonishi, and R. Hiura, "World’s first full-fledged communication robot” Wakamaru” capable of living with family and supporting persons," Mitsubishi Juko Giho, vol. 43, no. 1, pp. 44-45, 2006.
    37. J. K. Westlund et al., "Tega: a social robot," in 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016: IEEE, pp. 561-561.
    38. H. Ishiguro, T. Ono, M. Imai, T. Maeda, T. Kanda, and R. Nakatsu, "Robovie: an interactive humanoid robot," Industrial robot: An international journal, 2001. [39]. H. Kozima, M. P. Michalowski, and C. Nakagawa, "Keepon," International Journal of social robotics, vol. 1, no. 1, pp. 3-18, 2009.
    39. A. van Breemen, X. Yan, and B. Meerbeek, "iCat: an animated user-interface robot with personality," in Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems, 2005, pp. 143-144.
    40. E. Short et al., "How to train your dragonbot: Socially assistive robots for teaching children about nutrition through play," in The 23rd IEEE international symposium on robot and human interactive communication, 2014: IEEE, pp. 924-929.
    41. F. Klassner and S. D. Anderson, "Lego MindStorms: Not just for K-12 anymore," IEEE robotics & automation magazine, vol. 10, no. 2, pp. 12-18, 2003.

Recent Article