Can Human-Inspired Learning Behaviour Facilitate Human–Robot Interaction?

  • Alessandro CarfìEmail author
  • Jessica Villalobos
  • Enrique Coronado
  • Barbara Bruno
  • Fulvio Mastrogiovanni


The evolution of production systems for smart factories foresees a tight relation between human operators and robots. Specifically, when robot task reconfiguration is needed, the operator must be provided with an easy and intuitive way to do it. A useful tool for robot task reconfiguration is Programming by Demonstration (PbD). PbD allows human operators to teach a robot new tasks by showing it a number of examples. The article presents two studies investigating the role of the robot in PbD. A preliminary study compares standard PbD with human–human teaching and suggests that a collaborative robot should actively participate in the teaching process as human practitioners typically do. The main study uses a wizard of oz approach to determine the effects of having a robot actively participating in the teaching process, specifically by controlling the end-effector. The results suggest that active behaviour inspired by humans can lead to a more intuitive PbD.


Programming by demonstration Kinesthetic teaching Human robot interaction Industry 4.0 



The authors would like to thank the teachers and students of the vocational education and training schools “Centro Oratorio Votivo, Casa di Carità, Arti e Mestieri, Ovada” and “Istituto Tecnico Industriale Statale Italo Calvino, Genova” for their contribution to the drafting and execution of the experiments.


This work has been supported by the European Union Erasmus+ Programme via the Master programme European Master on Advanced Robotics Plus (EMARO+).

Compliance with ethical standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Akgun B, Cakmak M, Yoo JW, Thomaz AL (2012) Trajectories and keyframes for kinesthetic teaching: A human-robot interaction perspective. In: Proceedings of the seventh annual ACM/IEEE international conference on Human–Robot interaction, ACM, pp 391–398Google Scholar
  2. 2.
    Alexandrova S, Cakmak M, Hsiao K, Takayama L (2014) Robot programming by demonstration with interactive action visualizations. In: Proceedings of robotics: science and systems (RSS 2014), Berkeley, USAGoogle Scholar
  3. 3.
    Antonsson E, Mann R (1985) The frequency content of gait. J Biomech 18(1):39–47CrossRefGoogle Scholar
  4. 4.
    Argall B, Sauser E, Billard A (2010) Policy adaptation through tactile correction. In: Proceedings of the 2010 convention of the society for the study of artificial intelligence and simulation of behaviour (AISB 2010), Leicester, United KingdomGoogle Scholar
  5. 5.
    Biggs G, MacDonald B (2003) A survey of robot programming systems. In: Proceedings of the Australasian conference on robotics and automation (ACRA 2003), Brisbane, AustraliaGoogle Scholar
  6. 6.
    Billard A, Calinon S, Dillmann R, Schaal S (2008) Robot programming by demonstration. In: Springer handbook of robotics, Springer, pp 1371–1394Google Scholar
  7. 7.
    Calinon S, Guenter F, Billard A (2007) On learning, representing, and generalizing a task in a humanoid robot. Neural Netw 37(2):286–298Google Scholar
  8. 8.
    Chaudhuri B (1996) A new definition of neighborhood of a point in multi-dimensional space. Pattern Recogn Lett 17(1):11–17CrossRefGoogle Scholar
  9. 9.
    Dillmann R, Kaiser M, Ude A (1995) Acquisition of elementary robot skills from human demonstration. In: Proceedings of the international symposium on intelligent robotics systems (SIRS 1995), Pisa, ItalyGoogle Scholar
  10. 10.
    H Kagermann WW, Helbig J (2013) Recommendations for implementing the strategic initiative Industrie 4.0: Final report of the Industrie 4.0 working group. Produktion, Automatisierung und LogistikGoogle Scholar
  11. 11.
    Halbert DC (1984) Programming by example. Ph.D. thesis, University of California, Berkeley, USAGoogle Scholar
  12. 12.
    Inamura T, Kojo N, Inaba M (2006) Situation recognition and behavior induction based on geometric symbol representation of multimodal sensorimotor patterns. In: Proceeding of the 2006 IEEE/RSJ internationl conference on intelligent robots and systems (IROS 2006), Beijing, ChinaGoogle Scholar
  13. 13.
    Ito M, Noda K, Hoshino Y, Tani J (2006) Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model. Neural Netw 19(3):323–337CrossRefzbMATHGoogle Scholar
  14. 14.
    Kang SB, Ikeuchi K (1995) A robot system that observes and replicates grasping tasks. In: Proceedings of the 1995 IEEE international conference on computer vision (ICCV 1995), Boston, USAGoogle Scholar
  15. 15.
    Kormushev P, Calinon S, Caldwell DG (2011) Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Adv Robot 25(5):581–603CrossRefGoogle Scholar
  16. 16.
    Lambrecht J, Kleinsorge M, Rosenstrauch M, Krüger J (2013) Spatial programming for industrial robots through task demonstration. Int J Adv Robot Syst 10(5):254CrossRefGoogle Scholar
  17. 17.
    Liu S, Asada H (1993) Teaching and learning of deburring robots using neural networks. In: Proceedings of the 1993 IEEE international conference on robotics and automation (ICRA 1993), Atlanta, USAGoogle Scholar
  18. 18.
    Lucke D, Constantinescu C, Westkämper E (2008) Smart factory—a step towards the next generation of manufacturing. In: Manufacturing systems and technologies for the new frontier: The 41st CIRP conference on manufacturing systems, Tokyo, JapanGoogle Scholar
  19. 19.
    Massa D, Callegari M, Cristalli C (2015) Manual guidance for industrial robot programming. Ind Robot Int J 42(5):457–465CrossRefGoogle Scholar
  20. 20.
    Pais AL, Argall BD, Billard AG (2013) Assessing interaction dynamics in the context of robot programming by demonstration. Int J Soc Robot 5(4):477–490CrossRefGoogle Scholar
  21. 21.
    Suay HB, Toris R, Chernova S (2012) A practical comparison of three robot learning from demonstration algorithm. Int J Soc Robot 4(4):319–330CrossRefGoogle Scholar
  22. 22.
    Tung CP, Kak AC (1995) Automatic learning of assembly tasks using a DataGlove system. In: Proceeding of the 1995 IEEE/RSJ international conference on intelligent robots and systems (IROS 1995), Pittsburgh, USA, vol 1Google Scholar
  23. 23.
    Yang J, Xu Y, Chen CS (1994) Hidden Markov model approach to skill learning and its application to telerobotics. IEEE Trans Robot Autom 10(5):621–631CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Informatics, Bioengineering, Robotics and Systems EngineeringUniversity of GenoaGenoaItaly

Personalised recommendations