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Towards Endowing Collaborative Robots with Fast Learning for Minimizing Tutors’ Demonstrations: What and When to Do?

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)

Abstract

Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system – based on neural dynamics – that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario.

Keywords

Industrial robotics Assembly tasks Learning from demonstration Sequence order and timing Rapid learning Dynamic Neural Fields 

References

  1. 1.
    Amari, S.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27(2), 77–87 (1977).  https://doi.org/10.1007/BF00337259MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bicho, E., Erlhagen, W., Louro, L., e Silva, E.C.: Neuro-cognitive mechanisms of decision making in joint action: a human-robot interaction study. Hum. Mov. Sci. 30(5), 846–868 (2011).  https://doi.org/10.1016/j.humov.2010.08.012CrossRefGoogle Scholar
  3. 3.
    El Zaatari, S., Marei, M., Li, W., Usman, Z.: Cobot programming for collaborative industrial tasks: an overview. Robot. Auton. Syst. (2019).  https://doi.org/10.1016/j.robot.2019.03.003CrossRefGoogle Scholar
  4. 4.
    Erlhagen, W., Mukovskiy, A., Bicho, E., Panin, G., Kiss, C., Knoll, A., Van Schie, H., Bekkering, H.: Goal-directed imitation for robots: a bio-inspired approach to action understanding and skill learning. Robot. Auton. Syst. (2006).  https://doi.org/10.1016/j.robot.2006.01.004CrossRefGoogle Scholar
  5. 5.
    Erlhagen, W., Bicho, E.: The dynamic neural field approach to cognitive robotics. J. Neural Eng. 3(3), R36–R54 (2006).  https://doi.org/10.1088/1741-2560/3/3/R02CrossRefGoogle Scholar
  6. 6.
    Ferreira, F., Erlhagen, W., Bicho, E.: A dynamic field model of ordinal and timing properties of sequential events. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2011).  https://doi.org/10.1007/978-3-642-21738-8_42Google Scholar
  7. 7.
    Ferreira, F., Erlhagen, W., Bicho, E.: Multi-bump solutions in a neural field model with external inputs. Phys. D: Nonlinear Phenom. 326, 32–51 (2016).  https://doi.org/10.1016/j.physd.2016.01.009MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Ferreira, F., Erlhagen, W., Sousa, E., Louro, L., Bicho, E.: Learning a musical sequence by observation: a robotics implementation of a dynamic neural field model. In: IEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, pp. 157–162 (2014).  https://doi.org/10.1109/DEVLRN.2014.6982973
  9. 9.
    Kyrarini, M., Haseeb, M.A., Ristić-Durrant, D., Gräser, A.: Robot learning of industrial assembly task via human demonstrations. Auton. Robots 43(1), 239–257 (2019).  https://doi.org/10.1007/s10514-018-9725-6CrossRefGoogle Scholar
  10. 10.
    Orendt, E.M., Fichtner, M., Henrich, D.: Robot programming by non-experts: intuitiveness and robustness of one-shot robot programming. In: 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 192–199. IEEE (2016).  https://doi.org/10.1109/ROMAN.2016.7745110
  11. 11.
    Papanastasiou, S., Kousi, N., Karagiannis, P., Gkournelos, C., Papavasileiou, A., Dimoulas, K., Baris, K., Koukas, S., Michalos, G., Makris, S.: Towards seamless human robot collaboration: integrating multimodal interaction. Int. J. Adv. Manuf. Technol. 1–17 (2019).  https://doi.org/10.1007/s00170-019-03790-3CrossRefGoogle Scholar
  12. 12.
    Robotics, R.: Sawyer collaborative robot (2018). http://www.rethinkrobotics.com/sawyer/
  13. 13.
    Sandamirskaya, Y., Zibner, S.K.U., Schneegans, S., Schöner, G.: Using dynamic field theory to extend the embodiment stance toward higher cognition. New Ideas Psychol. 31(3), 322–339 (2013).  https://doi.org/10.1016/j.newideapsych.2013.01.002CrossRefGoogle Scholar
  14. 14.
    Schaal, S.: The new robotics towards human-centered machines. HFSP J. 1(2), 115–126 (2007).  https://doi.org/10.2976/1.2748612CrossRefGoogle Scholar
  15. 15.
    Schöner, G.: Dynamical systems approaches to cognition (January) (2012).  https://doi.org/10.1017/cbo9780511816772.007CrossRefGoogle Scholar
  16. 16.
    Sousa, E., Erlhagen, W., Ferreira, F., Bicho, E.: Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning. Neural Netw. 72, 123–139 (2015).  https://doi.org/10.1016/j.neunet.2015.09.002CrossRefGoogle Scholar
  17. 17.
    Wojtak, W., Ferreira, F., Louro, L., Bicho, E., Erlhagen, W.: Towards temporal cognition for robots: a neurodynamics approach. In: 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, ICDL-EpiRob 2017, pp. 407–412 (2018).  https://doi.org/10.1109/DEVLRN.2017.8329836

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Computer GraphicsUniversity of MinhoGuimaraesPortugal
  2. 2.Department of Mathematics and Applications, Center of MathematicsUniversity of MinhoGuimaraesPortugal
  3. 3.Department Industrial ElectronicsUniversity of MinhoGuimaraesPortugal

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