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Applied Intelligence

, Volume 49, Issue 11, pp 3834–3844 | Cite as

A robust interactive entertainment robot for robot magic performances

  • Kyle J. MorrisEmail author
  • Vladyslav Samonin
  • Jacky Baltes
  • John Anderson
  • Meng Cheng Lau
Article
  • 113 Downloads

Abstract

In recent years, there have been a number of popular robotics competitions whose intent is to advance the state of research by comparing embodied entries against one another in real time. The IEEE Humanoid application challenge is intended to broaden these by allowing more open ended entries, with a general theme within which entrants are challenged to create the most effective application involving a humanoid robot. This year’s theme was Robot Magic, and this paper describes our first-place winning entry in the 2017 competition, running on a ROBOTIS OP2 humanoid robot. We describe the overall agent design and contributions to perception, learning, control, and representation, together supporting a robust live robot magic performance.

Keywords

Human-robot interaction Robot magic Computer vision 

Notes

Acknowledgments

This research is partially supported by the “Chinese Language and Technology Center” and “Higher Education Sprout Project” of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan and also supported partially by the Ministry of Science and Technology, Taiwan, under Grants no. 107-2221-E-003 -024 -MY3 , MOST 107-2634-F-003-001, and MOST 107-2634-F-003-002.

References

  1. 1.
    Vogt K (2017) Why testing self-driving cars in sf is challenging but necessary. https://medium.com/kylevogt/why-testing-self-driving-cars-in-sf-is-challenging-but-necessary-77dbe8345927 medium.com [Online; posted 3-October-2017]
  2. 2.
    Baltes J, Anderson J (2009) Advancing artificial intelligence through minimalist humanoid robotics. In: Liu D, Wang L, Tan KC (eds) Design and control of intelligent robotic systems. Springer, Heidelberg, pp 355–376Google Scholar
  3. 3.
    Thrun S (2006) Winning the darpa grand challenge. In: European conference on machine learning. Springer, pp 4–4Google Scholar
  4. 4.
    Baltes J, Tu KY, Sadeghnejad S, Anderson J (2016) Hurocup: competition for multi-event humanoid robot athletes. Knowl Eng Rev, 1–14Google Scholar
  5. 5.
    Randi J (1993) Conjuring. St Martin’s PressGoogle Scholar
  6. 6.
    Lachapelle S (2015) Conjuring science: a history of scientific entertainment and stage magic in modern France. Palgrave MacMillan, LondonCrossRefGoogle Scholar
  7. 7.
    Tufte E, Swiss JI (1997) Explaining magic: pictorial instructions and disinformation design. In: Tufte E (ed) Visual explanations. Graphics Press, Cheshire, pp 54–71Google Scholar
  8. 8.
    Liang F, Gotham M, Johnson M, Shotton J (2017) Automatic stylistic composition of bach chorales with deep lstm. In: Proceedings of the 18th international society for music information retrieval conference (ISMIR-17), SuzhouGoogle Scholar
  9. 9.
    Google Experiments with Google - AI. https://experiments.withgoogle.com/ai
  10. 10.
    Schaeffer J, Burch N, Björnsson Y, Akihiro Kishimoto MM, Lake R, Lu P, Sutphen S (2007) Checkers is solved. Science 317:1518–1521MathSciNetCrossRefGoogle Scholar
  11. 11.
    Sturtevant N, Magerko B (eds) (2016) . AAAI Press, Palo AltoGoogle Scholar
  12. 12.
    Gerndt R, Seifert D, Baltes J, Sadeghnejad S, Behnke S (2015) Humanoid robots in soccer: robots versus humans in robocup 2050. IEEE Robot Autom Mag 22(3):147– 154CrossRefGoogle Scholar
  13. 13.
    Bagot J, Anderson J, Baltes J (2008) Vision-based multi-agent slam for humanoid robots. In: Proceedings of CIRAS-2008), pp 171–176Google Scholar
  14. 14.
    Baltes J, Cheng CT, Bagot J, Anderson J Vision-based obstacle run for teams of humanoid robots (demonstrated system). In: Proceedings of AAMAS-2011, Taipei, pp 1319–1320Google Scholar
  15. 15.
  16. 16.
    Lim H, Kang Y, Lee J, Kim J, You BJ (2008) Multiple humanoid cooperative control system for heterogeneous humanoid team. In: The 17th IEEE international symposium on robot and human interactive communication, 2008. RO-MAN 2008. IEEE, pp 231–236Google Scholar
  17. 17.
    Farazi H, Allgeuer P, Ficht G, Brandenburger A, Pavlichenko D, Schreiber M, Behnke S (2016) Robocup 2016 humanoid teensize winner nimbro: robust visual perception and soccer behaviors. In: RoboCup 2016: Robot World Cup XX [Leipzig, Germany, June 30 - July 4, 2016], pp 478–490Google Scholar
  18. 18.
    Mastrogiovanni F, Sgorbissa A (2013) A behaviour sequencing and composition architecture based on ontologies for entertainment humanoid robots. Robot Auton Syst 61(2):170– 183CrossRefGoogle Scholar
  19. 19.
    Kuroki Y (2001) A small biped entertainment robot. In: MHS2001. Proceedings of 2001 International symposium on micromechatronics and human science (Cat. No.01TH8583), pp 3–4Google Scholar
  20. 20.
    Koretake R, Kaneko M, Higashimori M (2015) The robot that can achieve card magic. ROBOMECH J 2(1):5CrossRefGoogle Scholar
  21. 21.
    Nuñez D, Tempest M, Viola E, Breazeal C (2014) An initial discussion of timing considerations raised during development of a magician-robot interaction. In: Proc. ACM/IEEE Workshop on timing in human-robot interaction HRIGoogle Scholar
  22. 22.
    Tamura Y, Yano S, Osumi H (2014) Modeling of human attention based on analysis of magic. In: Proceedings of the 2014 ACM/IEEE international conference on human-robot interaction. HRI ’14. ACM, New York, pp 302–303Google Scholar
  23. 23.
    Redacted A (2018) Interaction and learning in a humanoid robot magic performance. In: Proceedings of the AAAI spring symposium on integrated representation, reasoning, and learning in roboticsGoogle Scholar
  24. 24.
    Kotsiantis SB, Zaharakis I, Pintelas P (2007) Supervised machine learning: a review of classification techniquesGoogle Scholar
  25. 25.
    Arkin RC (1998) Behavior-based robotics. MIT Press, CambridgeGoogle Scholar
  26. 26.
    Anderson J, Baltes J (2006) An agent-based approach to introductory robotics using robotic soccer. Int J Robot Autom, 21(2)Google Scholar
  27. 27.
    Liu T, Baltes J, Anderson J (2005) Archangel, a flexible and intuitive architecture for intelligent mobile robots. In: Proceedings of CIRAS-2005. SingaporeGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Autonomous Agents Laboratory, Department of Computer ScienceUniversity of ManitobaWinnipegCanada
  2. 2.Department of Electrical EngineeringNational Taiwan Normal UniversityTaipeiTaiwan

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