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

  • Ana Cunha
  • Flora Ferreira
  • Wolfram Erlhagen
  • Emanuel Sousa
  • Luís Louro
  • Paulo Vicente
  • Sérgio Monteiro
  • Estela BichoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)


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.


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


  1. 1.
    Amari, S.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biol. Cybern. 27(2), 77–87 (1977). 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). Scholar
  3. 3.
    El Zaatari, S., Marei, M., Li, W., Usman, Z.: Cobot programming for collaborative industrial tasks: an overview. Robot. Auton. Syst. (2019). 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). Scholar
  5. 5.
    Erlhagen, W., Bicho, E.: The dynamic neural field approach to cognitive robotics. J. Neural Eng. 3(3), R36–R54 (2006). 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). 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). 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).
  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). 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).
  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). Scholar
  12. 12.
    Robotics, R.: Sawyer collaborative robot (2018).
  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). Scholar
  14. 14.
    Schaal, S.: The new robotics towards human-centered machines. HFSP J. 1(2), 115–126 (2007). Scholar
  15. 15.
    Schöner, G.: Dynamical systems approaches to cognition (January) (2012). 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). 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).

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ana Cunha
    • 1
    • 3
  • Flora Ferreira
    • 2
  • Wolfram Erlhagen
    • 2
  • Emanuel Sousa
    • 1
  • Luís Louro
    • 3
  • Paulo Vicente
    • 3
  • Sérgio Monteiro
    • 3
  • Estela Bicho
    • 3
    Email author
  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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