JRM Vol.26 No.1 pp. 51-58
doi: 10.20965/jrm.2014.p0051


Reconstruction of Human Skills by Using PCA and Transferring them to a Robot

Masahiro Takeuchi*, Jun Shimodaira**, Yuki Amaoka**,
Shinsuke Hamatani**, Hiroaki Hirai**, and Fumio Miyazaki**

*Akashi National College of Technology, 679-3 Nishioka, Uozumi-cho, Skashi-shi, Hyogo 674-8501, Japan

**Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka-shi, Osaka 560-8531, Japan

May 19, 2013
September 6, 2013
February 20, 2014
generalized motor program, principal component analysis, human skill transfer

This paper discusses human skills enabling rapid adaptation to a changing environment, e.g., when a human table tennis player hits an incoming ball, and describes how to transfer these skills to a robot. Human skills are classified into motor and cognitive. Motor skills are functions involving precise limb movement with the intent to perform a specific act, i.e., hitting a ball. Cognitive skills are functions involving meaningful responses to external stimuli. We extract these skills from observing human movement using principal component analysis and generalize these skills as a schema for a generalized motor program. We also describe table tennis matches between a human opponent and a robot to which these skills have been transferred.

  1. [1] A. Fod, M. J. Mataric, and O. C. Jenkins, “Automated Derivation of Primitives for Movement Classification,” Autonomous Robots, Vol.12, No.1, pp. 39-54, 1993.
  2. [2] O. C. Jenkins and M. J. Mataric, “Deriving Action and Behavior Primitives from Human Motion,” 2002 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2551-2556, 2002.
  3. [3] J. Nakanishi, A. J. Ijspeert, S. Schaal, and G. Cheng, “Learning Movement Primitives for Imitation Learning in Humanoid Robots,” J. of the Robotics Society of Japan, Vol.22, No.2, pp. 165-170, 2004.
  4. [4] R. A. Schmidt and T. D. Lee, “MOTOR CONTROL AND LEARNING,” Human Kinetics, 1999.
  5. [5] D. A. Tyldesley and H. T. Whiting, “Operational timing,” J. of Human Movement Studies, Vol.1, No.4, pp. 172-177, 1975.
  6. [6] A. Daffertshofer, C. J. C. Lamoth, O. G. Meijer, and P. L. Beek, “PCA in Studying Coordination and Variability: A Tutorial,” Clinical Biomechanics, Vol.19, Issue 4, pp. 415-428, 2004.
  7. [7] M. Matsushima, T. Hashimoto, M. Takeuchi, and F. Miyazaki, “A Learning Approach to Robotics Table Tennis,” IEEE Tran. Robotics, Vol.21, No.4, pp. 767-771, 2005.
  8. [8] F. Miyazaki, M. Matsushima, and M. Takeuchi, “Learning to dynamically manipulate: A table tennis robot controls a ball rallies with a human being,” Advances in Robot Control, Springer, pp. 317-341, 2006.
  9. [9] B. Lim, S. Ra, and F. Park, “Movement primitives, principal component analysis, and the efficient generation of natural motions,” in Proc. IEEE Int. Conf. Robot. Autom., pp. 4630-4635, 2005.
  10. [10] P. K. Artemiadis and K. J. Kyriakopoulos, “EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings,” IEEE Tran. on Robotics, Vol.26, No.2, pp. 393-398, 2010.
  11. [11] R. A. Schmidt, “A Schema Theory Discrete Motor Skill Learning,” Psychological Review, Vol.82, Issue 4, pp. 225-260, 1975.

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Last updated on Jan. 23, 2018