access icon openaccess Segmentation and generalisation for writing skills transfer from humans to robots

This is an open access article published by the IET in partnership with Shenzhen University under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 30/09/2018, Accepted 12/12/2018, Revised 17/11/2018, Published 10/01/2019

In this study, the authors present an enhanced generalised teaching by demonstration technique for a KUKA iiwa robot. Movements are recorded from a human operator, and then the recorded data are sent to be segmented via MATLAB by using the difference method (DV). The outputted trajectories data are used to model a non-linear system named dynamic movement primitive (DMP). For the purpose of learning from multiple demonstrations correctly and accurately, the Gaussian mixture model is employed for the evaluation of the DMP in order to modelling multiple trajectories by the teaching of demonstrator. Furthermore, a synthesised trajectory with smaller position errors in 3D space has been successfully generated by the usage of the Gaussian mixture regression algorithm. The proposed approach has been tested and demonstrated by performing a Chinese characters writing task with a KUKA iiwa robot.

Inspec keywords: regression analysis; human-robot interaction; Gaussian processes; difference equations; humanoid robots; Matlab; mixture models; control engineering computing; robot programming; trajectory control; nonlinear control systems; motion control

Other keywords: KUKA iiwa robot; trajectories modelling; Gaussian mixture model; Gaussian mixture regression algorithm; difference method; MATLAB; trajectories data; nonlinear system; teaching by demonstration technique; DMP; writing skills transfer; dynamic movement primitive

Subjects: Control engineering computing; Mathematical analysis; Other topics in statistics; Robotics; Nonlinear control systems; Spatial variables control; Systems analysis and programming

References

    1. 1)
      • 12. Ananthanarayanan, R., Esser, S.K., Simon, H.D., et al: ‘The cat is out of the bag: cortical simulations with 10 9 neurons, 10 13 synapses’. Proc. of the Conf. on High Performance Computing Networking, Storage and Analysis, Portland, OR, USA, 2009, p. 63.
    2. 2)
      • 3. Hogan, N.: ‘Controlling impedance at the man/machine interface’. Proc. 1989 IEEE Int. Conf. on Robotics and Automation, Scottsdale, Arizona, USA, 1989, pp. 1626–1631.
    3. 3)
      • 14. Green, S.A., Billinghurst, M., Chen, X.Q., et al: ‘Human-robot collaboration: A literature review and augmented reality approach in design’, Int. J. Adv. Robot. Syst., 2008, 5, (1), p. 1.
    4. 4)
      • 26. Abdul Nazeer, K.A., Sebastian, M.P.: ‘Improving the accuracy and efficiency of the k-means clustering algorithm’. Proc. of the World Congress on Engineering, London, U.K, 2009, vol. 1, pp. 1–3.
    5. 5)
      • 20. Li, M.-Z., Ma, Z.-Q., Shan, Y., et al: ‘Adaptive background update based on Gaussian mixture model under complex condition’, Jisuanji Yingyong/J. Comput. Appl., 2011, 31, (7), pp. 1831–1834.
    6. 6)
      • 5. Poczter, S.L., Jankovic, L.M.: ‘The google car: driving toward a better future?’, J. Bus. Case Stud. (Online), 2014, 10, (1), p. 7.
    7. 7)
      • 15. Brogårdh, T.: ‘Present and future robot control development an industrial perspective’, Annu. Rev. Control, 2007, 31, (1), pp. 69–79.
    8. 8)
      • 19. Son, H., Kim, C., Kim, H., et al: ‘Trend analysis of research and development on automation and robotics technology in the construction industry’, Korean Soc. Civ. Eng. KSCE J. Civ. Eng., 2010, 14, (2), pp. 131–139.
    9. 9)
      • 18. Bischoff, R., Kurth, J., Schreiber, G., et al: ‘The kuka-dlr lightweight robot arm-a new reference platform for robotics research and manufacturing’. Robotics (ISR), 2010 41st Int. Symp. on and 2010 6th German Conf. on robotics (ROBOTIK), Munich, Germany, 2010, pp. 1–8.
    10. 10)
      • 25. Huang, Z.-K., Chau, K.-W.: ‘A new image thresholding method based on Gaussian mixture model’, Appl. Math. Comput., 2008, 205, (2), pp. 899–907.
    11. 11)
      • 11. Modha, D.S., Ananthanarayanan, R., Esser, S.K., et al: ‘Cognitive computing’, Commun. ACM, 2011, 54, (8), pp. 62–71.
    12. 12)
      • 21. Xuan, G., Zhang, W., Chai, P.: ‘Em algorithms of Gaussian mixture model and hidden markov model’. Proc. 2001 Int. Conf. on Image Processing (Cat. No. 01CH37205), Thessaloniki, Greece, 2001, vol. 1, pp. 145–148.
    13. 13)
      • 13. Ahmed, M.N., Toor, A.S., O'Neil, K., et al: ‘Cognitive computing and the future of health care cognitive computing and the future of healthcare: the cognitive power of IBM watson has the potential to transform global personalized medicine’, IEEE. Pulse., 2017, 8, (3), pp. 4–9.
    14. 14)
      • 4. Libbrecht, M.W., Noble, W.S.: ‘Machine learning applications in genetics and genomics’, Nat. Rev. Genetics, 2015, 16, (6), p. 321.
    15. 15)
      • 8. Oksanen, T., Kostamo, J., Tamminen, P., et al: ‘Robot competition as a teaching and learning platform’. Proc. the 18th IFAC World Congress, Milano, August 2011, pp. 5176–5181.
    16. 16)
      • 10. Li, C., Yang, C., Ju, Z., et al: ‘An enhanced teaching interface for a robot using DMP and GMR’, Int. J. Intell. Robot. Appl., 2018, 2, (1), pp. 110–121.
    17. 17)
      • 24. Ijspeert, A.J., Nakanishi, J., Hoffmann, H., et al: ‘Dynamical movement primitives: learning attractor models for motor behaviors’, Neural Comput., 2013, 25, (2), pp. 328–373.
    18. 18)
      • 16. Curtis, A.R., Reid, J.K.: ‘The choice of step lengths when using differences to approximate jacobian matrices’, IMA J. Appl. Math., 1974, 13, (1), pp. 121–126.
    19. 19)
      • 1. Arkin, R.C., Fujita, M., Takagi, T., et al: ‘An ethological and emotional basis for human–robot interaction’, Robot. Auton. Syst., 2003, 42, (3–4), pp. 191–201.
    20. 20)
      • 6. Cao, L., Sun, F., Li, H., et al: ‘Advancing the incremental fusion of robotic sensory features using online multi-kernel extreme learning machine’, Front. Comput. Sci., 2017, 11, (2), pp. 276–289.
    21. 21)
      • 17. Kwong, M.K., Zettl, A.: ‘Norm inequalities for derivatives and differences’ (Springer, Springer-Verlag Berlin Heidelberg New York, US, 2006).
    22. 22)
      • 23. Schaal, S.: ‘Dynamic movement primitives-a framework for motor control in humans and humanoid robotics’, in Kimura, H., Tsuchiya, K., Ishiguro, A., et al (Eds.): ‘Adaptive motion of animals and machines’ (Springer, Springer, Tokyo, Japan, 2006), pp. 261–280.
    23. 23)
      • 9. Schaal, S., Peters, J., Nakanishi, J., et al: ‘Learning movement primitives’. Robotics Research, The Eleventh Int. Symp., 2005, pp. 561–572.
    24. 24)
      • 22. Boyd, S., Parikh, N., Chu, E., et al: ‘Distributed optimization and statistical learning via the alternating direction method of multipliers’ (Foundations and Trends® in Machine Learning, Hanover, MA, USA, 2011), vol. 3, no. 1, pp. 1–122.
    25. 25)
      • 2. Bartneck, C., Forlizzi, J.: ‘Shaping human-robot interaction: understanding the social aspects of intelligent robotic products’. CHI'04 Extended Abstracts on Human Factors in Computing Systems, Vienna, Austria, 2004, pp. 1731–1732.
    26. 26)
      • 7. Li, C., Yang, C., Wan, J., et al: ‘Teleoperation control of baxter robot using kalman filter-based sensor fusion’, Syst. Sci. Control Eng., 2017, 5, (1), pp. 156–167.
    27. 27)
      • 27. Tatiraju, S., Mehta, A.: ‘Image segmentation using k-means clustering, EM and normalized cuts’. University of California Irvine, 2008.
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