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Imitation learning of humanoid locomotion using the direction of landing foot

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  • Robotics and Automation
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Abstract

Since it is quite difficult to create motions for humanoid robots having a fairly large number of degrees of freedom, it would be very convenient indeed if robots could observe and imitate what they want to create. To this end, this paper discusses how humanoid robots can learn through imitation taking into consideration the fact that demonstrator and imitator robots may have different kinematics and dynamics. As part of a wider interest in humanoid motion generation in general, this work mainly investigates how imitator robots adapt a reference locomotion gait copied from a demonstrator robot. Specifically, the self-adjusting adaptor is proposed, where the perceived locomotion pattern is modified to keep the direction of the lower leg contacting the ground identical between the demonstrator and the imitator, and to sustain dynamic stability by controlling the position of the center of mass. The validity of the proposed scheme is verified through simulations on OpenHRP and real experiments.

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References

  1. M. Vukobratovic and D. Juricic, “Contribution to the synthesis of biped gait,” IEEE Trans. Bio-Med. Eng., vol. BME-16, no.1, pp. 1–6, 1969.

    Article  Google Scholar 

  2. M. Vukobratovic, “How to control artificial anthropomorphic systems,” IEEE Trans. Syst., Man, Cybern., vol. SMC-3, no.5, pp. 497–507, 1973.

    Article  Google Scholar 

  3. S. Kajita, O. Matsumoto, and M. Saigo, “Real-time 3D walking pattern generation for a biped robot with telescopic legs,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 2299–2306, 2001.

  4. J. Pratt, C.-M. Chew, A. Torres, P. Dilworth, and G. Pratt, “Virtual model control: an intuitive approach for biped locomotion,” The Int. Journal of Robotics Research, vol. 20, no. 2, pp. 129–143, 2001.

    Article  Google Scholar 

  5. G. Endo, J. Nakanishi, J. Morimoto, and G. Cheng, “Experimental studies of a neural oscillator for biped locomotion with QRIO,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 598–604, 2005.

  6. C. Nehaniv and K. Dautenhahn, “Of hummingbirds and helicopters: an algebraic framework for interdisciplinary studies of imitation and its applications,” An Interdisciplinary Approaches to Robot Learning, J. Demiris and A. Birk (Eds.), World Scientific Publishing Co., pp. 136–161, 2000.

  7. A. Billard and R. Siegwart, “Robot learning from demonstration,” Robotics and Autonomous Systems, vol. 47, pp. 65–67, 2004.

    Article  Google Scholar 

  8. J. Demiris and G. Hayes, “Do robots ape?,” Proc. AAAI Fall Symp. on Socially Intelligent Agents, pp. 28–30, 1997.

  9. A. Dasgupta and Y. Nakamura, “Making feasible walking motion of humanoid robots from human motion capture data,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1044–1049, 1999.

  10. T. Inamura, I. Toshima, H. Tanie, and Y. Nakamura, “Embodied symbol emergence based on mimesis theory,” The Int. Journal of Robotics Research, vol. 23, no. 4–5, pp. 363–377, 2004.

    Article  Google Scholar 

  11. A. J. Ijspeert, J. Nakanishi, and S. Schaal, “Movement imitation with nonlinear dynamical systems in humanoid robots,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 1398–1403, 2002.

  12. S. Schaal, “Is Imitation learning the way to humanoid robots?,” Trends in Cognitive Science, vol. 3, no. 6, pp. 233–242, 1999.

    Article  Google Scholar 

  13. K. Samejima, K. Katagiri, K. Doya, and M. Kawato, “Symbolization and imitation learning of motion sequence using competitive modules,” Trans. of the Institute of Electronics, Information, and Communication Engineers, vol. J85-D-II, no. 1, pp. 90–100, 2002.

    Google Scholar 

  14. K. Samejima, K. Doya, and M. Kawato, “Intermodule credit assignment in modular reinforcement learning,” Neural Networks, vol. 16, no. 7, pp. 985–994, 2003.

    Article  Google Scholar 

  15. Y. Nakamura and M. Ghodoussi, “Dynamics computation of closed-link robot mechanisms with nonredundant and redundant actuators,” IEEE Trans. on Robotics and Automation, vol. 5, no. 3, pp. 294–302, 1989.

    Article  Google Scholar 

  16. Y. Nakamura and K. Yamane, “Dynamics computation of structure-varying kinematic chains and its application to human figures,” IEEE Trans. on Robotics and Automation, vol. 16, no. 2, pp. 124–134, 2000.

    Article  Google Scholar 

  17. K. Yamane and Y. Nakamura, “Dynamics filter — concept and implementation of online motion generator for human figures,” IEEE Trans. on Robotics and Automation, vol. 19, no. 3, pp. 421–432, 2003.

    Article  Google Scholar 

  18. W. Yang and N. Y. Chong, “Goal-directed imitation with self-adjusting adaptor based on a neural oscillator network,” Proc. Int. Conf. on Advanced Robotics, pp. 404–410, 2005.

  19. W. Yang, N. Y. Chong, C. Kim, and B. J. You, “Locomotion Imitation of humanoid using goaldirected self-adjusting adaptor,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 5650–5656, 2006.

  20. http://www.is.aist.go.jp/humanoid/openhrp/English/indexE.html

  21. J.-S. Kong, E.-H. Lee, B.-H. Lee, and J.-G. Kim, “Study on the real-time walking control of a humanoid robot using fuzzy algorithm,” Int. Journal of Control, Automation, and Systems, vol. 6, no. 4, pp. 551–558, 2008.

    MathSciNet  Google Scholar 

  22. C. A. A. Calderon and H. Hu, “Robot imitation from human body movements,” Proc. Int. Symp. on Imitation in Animals and Artifacts, pp. 1–9, 2005.

  23. M. J. Mataric, “Imitation in animals and artifacts,” Sensory-Motor Primitives as a Basis for Imitation: Linking Perception to Action and Biology to Robotics, pp. 392–422, The MIT Press, Cambridge, MA, 2002.

    Google Scholar 

  24. M. J. Mataric and M. Pomplun, “Fixation behavior in observation and imitation of human movement,” Cognitive Brain Research, vol. 7, no. 2, pp. 191–202, 1998.

    Article  Google Scholar 

  25. W. Yang, N. Y. Chong, C. Kim, and B. J. You, “Self-adapting humanoid locomotion using a neural oscillator network,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 309–316, 2007.

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Authors and Affiliations

  1. Center for Cognitive Robotics Research, Korea Institute of Science and Technology, Seoul, Korea

    Woosung Yang

  2. School of Information Science, Japan Advanced Institute of Science & Technology, Ishikawa, Japan

    Nak Young Chong

Authors
  1. Woosung Yang
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  2. Nak Young Chong
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Correspondence to Woosung Yang.

Additional information

Recommended by Editorial Board member Hyoukryeol Choi under the direction of Editor Jae-Bok Song. This work was conducted as a program for the “Fostering Talent in Emergent Research Fields” in Special Coordination Funds for the Promotion of Science and Technology by the Ministry of Education, Culture, Sports, Science and Technology of Japan. This work was also supported in part by MIC and IITA of Korea through IT Leading R&D Support Project. [2009-S028-01, Development of Cooperative Network-based Humanoids Technology]

Woosung Yang received his B.S. and M.S. degrees in Mechanical Engineering from Sogang University, Seoul, Korea in 2001 and 2003, and his Ph.D. degree in the School of Information Science from Japan Advanced Institute of Science and Technology (JAIST), Ishikawa, Japan in 2007, respectively. Since 2007, he has been a Post-doctoral Researcher in Center for Cognitive Robotics, Korea Institute of Science and Technology. His research interests include intelligent control theory, biologically inspired control and system, humanoids, and actuator controls for small form factor precision devices.

Nak Young Chong received his B.S., M.S., and Ph.D. in Mechanical Engineering from Hanyang University, Seoul, Korea in 1987, 1989, and 1994, respectively. He was senior researcher at Daewoo Heavy Industries Ltd. (1994–98), visiting researcher at MEL in Tsukuba, Japan (1995–96), and postdoctoral researcher at KIST (1998). From 1998–2007, he was on the research staff of AIST in Tsukuba, Japan. In 2003, he joined the faculty of JAIST as Associate Professor of Information Science. Dr. Chong served as Co-chair of the IEEE RAS Technical Committee on Networked Robots (2004–06), and the Fujitsu Scientific Systems Robotics WG (2004–06) and Robot Information Processing WG (2006–08), respectively. He visited Northwestern University (2001) and Georgia Tech (2008–09). He is currently serving as Associate Editor of the IEEE Transactions on Robotics and the International Journal of Assistive Robotics and Systems. He is the Korea Robotics Society director of international cooperation, and a member of IEEE, RSJ, and SICE.

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Yang, W., Chong, N.Y. Imitation learning of humanoid locomotion using the direction of landing foot. Int. J. Control Autom. Syst. 7, 585–597 (2009). https://doi.org/10.1007/s12555-009-0410-6

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  • DOI: https://doi.org/10.1007/s12555-009-0410-6

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