JACIII Vol.23 No.3 pp. 584-591
doi: 10.20965/jaciii.2019.p0584


Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners

Ryosuke Tanaka, Jinseok Woo, and Naoyuki Kubota

Intelligent Mechanical Systems, Graduate School of System Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

November 30, 2018
February 19, 2019
May 20, 2019
robot partner, human-robot interaction, cognitive development system, instructed learning, information support

The research and development of robot partners have been actively conducted to support human daily life. Human-robot interaction is one of the important research field, in which verbal and nonverbal communication are essential elements for improving the interactions between humans and robots. Thus, the purpose of this research was to establish a method to adapt a human-robot interaction mechanism for robot partners to various situations. In the proposed system, the robot needs to analyze the gestures of humans to interact with them. Humans have the ability to interact according to dynamically changing environmental conditions. Therefore, when robots interact with a human, it is necessary for robots to interact appropriately by correctly judging the situation according to human gestures to carry out natural human-robot interaction. In this paper, we propose a constructive methodology on a system that enables nonverbal communication elements for human-robot interaction. The proposed method was validated through a series of experiments.

Cite this article as:
R. Tanaka, J. Woo, and N. Kubota, “Nonverbal Communication Based on Instructed Learning for Socially Embedded Robot Partners,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 584-591, 2019.
Data files:
  1. [1] K. W. Fischer, “A theory of cognitive development: The control and construction of hierarchies of skills,” Psychological Review, Vol.87, No.6, p. 477, 1980.
  2. [2] M. H. Bickhard and L. Terveen, “Foundational issues in artificial intelligence and cognitive science: Impasse and solution,” Vol.109, Elsevier, 1996.
  3. [3] C. Breazeal, C. D. Kidd, A. L. Thomaz, G. Hoffman, and M. Berlin, “Effects of nonverbal communication on efficiency and robustness in human-robot teamwork,” 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2005), pp. 708-713, 2005.
  4. [4] A. G. Brooks and R. C. Arkin, “Behavioral overlays for non-verbal communication expression on a humanoid robot,” Autonomous Robots, Vol.22, No.1, pp. 55-74, 2007.
  5. [5] A. Mehrabian, “Nonverbal communication,” Routledge, 2017.
  6. [6] C. Rich, B. Ponsler, A. Holroyd, and C. L. Sidner, “Recognizing engagement in human-robot interaction,” 2010 5th ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI), pp. 375-382, 2010.
  7. [7] M. Tomasello, M. Carpenter, and U. Liszkowski, “A new look at infant pointing,” Child Development, Vol.78, No.3, pp. 705-722, 2007.
  8. [8] B. L. Davies, “Grice’s cooperative principle: meaning and rationality,” J. of Pragmatics, Vol.39, No.12, pp. 2308-2331, 2007.
  9. [9] M. Tomasello, “Origins of human communication,” MIT Press, 2010.
  10. [10] R. W. Gibbs Jr., “Embodiment and cognitive science,” Cambridge University Press, 2005.
  11. [11] M. Hirano, N. Hanajima, K. Urita, S. Muto, Y. Muraoka, and M. Ohata, “Development of an Exercise Support System for the Elderly Which Uses a Small Humanoid Robot,” J. Robot. Mechatron., Vol.25, No.6, pp. 939-948, 2013.
  12. [12] J. Woo, J. Botzheim, and N. Kubota, “System Integration for Cognitive Model of a Robot Partner,” Intelligent Automation & Soft Computing, pp. 1-14, DOI: 10.1080/10798587.2017.1364919, 2017.
  13. [13] J. Woo and N. Kubota, “Interaction content design for information support based on robot partner,” 2017 10th Int. Conf. on Human System Interactions (HSI), pp. 155-160, 2017.
  14. [14] J. Woo, J. Botzheim, and N. Kubota, “Facial and gestural expression generation for robot partners,” 2014 Int. Symp. on Micro-NanoMechatronics and Human Science (MHS), pp. 1-6, 2014.
  15. [15] S. A. Arduino, “Arduino,” Arduino LLC, 2015.
  16. [16] Apple Inc., “Developer (iOS system),” 2018.
  17. [17] J. Woo, J. Botzheim, and N. Kubota, “A Modular Cognitive Model of Socially Embedded Robot Partners for Information Support,” Robomech J., Vol.4, No.1, p. 10, 2017.
  18. [18] J. Woo, J. Botzheim, and N. Kubota, “Verbal conversation system for a socially embedded robot partner using emotional model,” 2015 24th IEEE Int. Symp. on Robot and Human Interactive Communication (RO-MAN), pp. 37-42, 2015.
  19. [19] J. Woo, J. Botzheim, and N. Kubota, “A Socially Interactive Robot Partner Using Content-Based Conversation System for Information Support,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 989-997, 2018.
  20. [20] J. Szeles, N. Kubota, and J. Woo, “Weather forecast support system implemented into robot partner for supporting elderly people using fuzzy logic,” 2017 Joint 17th World Congress of Int. Fuzzy Systems Association and 9th Int. Conf. on Soft Computing and Intelligent Systems (IFSA-SCIS), pp. 1-5, 2017.
  21. [21] P. Robinson, “Individual differences and instructed language learning,” Vol.2, John Benjamins Publishing, 2002.
  22. [22] N. Kubota, Y. Tomioka, and M. Abe, “Temporal coding in spiking neural network for gestures recognition of a partner robot,” SCIS&ISIS 2006, pp. 737-742, 2006.
  23. [23] W. Gerstner and W. M. Kistler, “Spiking neuron models: Single neurons, populations, plasticity,” Cambridge University Press, 2002.
  24. [24] S. Loiselle, J. Rouat, D. Pressnitzer, and S. Thorpe, “Exploration of rank order coding with spiking neural networks for speech recognition,” 2005 IEEE Int. Joint Conf. on Neural Networks (IJCNN’05), Proc., Vol. 4, pp. 2076-2080, 2005.
  25. [25] Z. Zhang, “Microsoft kinect sensor and its effect,” IEEE Multimedia, Vol.19, No.2, pp. 4-10, 2012.
  26. [26] T. Kohonen, “Exploration of very large databases by self-organizing maps,” Proc. of Int. Conf. on Neural Networks (ICNN’97), Vol. 1, pp. PL1-PL6, 1997.
  27. [27] J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A k-means clustering algorithm,” J. of the Royal Statistical Society, Series C (Applied Statistics), Vol.28, No.1, pp. 100-108, 1979.
  28. [28] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, pp. 625-632, MIT Press, 1995.

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Last updated on Sep. 19, 2019