Measuring Progress on an Approach for Interactive Learning of Context-Driven Actions

  • Martin F. StoelenEmail author
  • Davide Marocco
  • Fabio Bonsignorio
  • Angelo Cangelosi
Part of the Cognitive Systems Monographs book series (COSMOS, volume 36)


This chapter is focused on benchmarking robot learning of physical manipulation tasks, in particular where the task execution is strongly driven by the task context and where the learning is interactive. By ‘context’ is here implied the full set of sensory input available to an embodied platform.


  1. 1.
    Metta, G., Sandini, G., Vernon, D., Natale, L., Nori, F.: The iCub humanoid robot: an open platform for research in embodied cognition. In: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, pp. 50–56 (2008)Google Scholar
  2. 2.
    Cangelosi, A., Schlesinger, M.: Developmental Robotics: From Babies to Robots. MIT Press, Cambridge (2015)CrossRefGoogle Scholar
  3. 3.
    Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318(5853), 1088–1093 (2007)Google Scholar
  4. 4.
    Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)CrossRefGoogle Scholar
  5. 5.
    Ijspeert, A.J., Nakanishi J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Advances in Neural Information Processing Systems, pp. 1547–1554 (2003)Google Scholar
  6. 6.
    Tani, J., Ito, M.: Self-organization of behavioral primitives as multiple attractor dynamics: a robot experiment. IEEE Trans. Syst., Man, Cybern.-Part A: Syst. Hum. 3(4), 481–488 (2003)CrossRefGoogle Scholar
  7. 7.
    Yamashita, Y., Tani, J.: Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Comput. Biol. 4(11) (2008)Google Scholar
  8. 8.
    Waegeman, T., et al.: Modular reservoir computing networks for imitation learning of multiple robot behaviors. In: Proceedings of IEEE International Symposium on Computer Intelligence in Robotics and Automation, pp. 27–32 (2009)Google Scholar
  9. 9.
    Calinon, S., Billard, A.: What is the teacher’s role in robot programming by demonstration? Toward benchmarks for improved learning. Interact. Stud. 8(3), 441–464 (2007)CrossRefGoogle Scholar
  10. 10.
    Cederborg, T., Li, M., Baranes, A., Oudeyer, P.Y.: Incremental local online gaussian mixture regression for imitation learning of multiple tasks. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 267–274 (2010)Google Scholar
  11. 11.
    Bonsignorio, F., Hallam, J., del Pobil, A.P.: Good experimental methodologies in robotics: state of the art and perspectives. In: Proceedings of the Workshop Performance Evaluation Benchmarking Intelligent Robots Systems, IEEE/RSJ International Conference Intelligent Robots Systems, San Diego, CA (2007)Google Scholar
  12. 12.
    Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)Google Scholar
  13. 13.
    Lampe, A., Chatila, R.: Performance measure for the evaluation of mobile robot autonomy. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4057–4062. Orlando, Florida (2006)Google Scholar
  14. 14.
    Young, S.H., Mazzuchi, T.A., Sarkani, S.: A model-based framework for predicting performance in self-adaptive systems. Proc. Comput. Sci. 28, 513–521 (2014)CrossRefGoogle Scholar
  15. 15.
    Stoelen, M.F., et al.: Online learning of sensorimotor interactions using a neural network with time-delayed inputs. In: IEEE Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), San Diego, USA (2012)Google Scholar
  16. 16.
    Stoelen, M.F., Marocco, D., Cangelosi, A., Bonsignorio, F., Balaguer, C.: Predictive Hebbian association of time-delayed inputs with actions in a developmental robot platform. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 700–707, Antibes, France (2014)Google Scholar
  17. 17.
    Stoelen, M.F., Tejada, V.F., Jardón, A., Balaguer, B., Bonsignorio, F.: Towards replicable experiments on distributed and adaptive shared control systems. IEEE Robot. Autom. Mag. 22(4), 137–146 (2015)CrossRefGoogle Scholar
  18. 18.
    Stoelen, M.F., Bonsignorio, F., Cangelosi, A.: Co-exploring actuator antagonism and bio-inspired control in a printable robot arm, to be presented, 4th International Conference on the Simulation of Adaptive Behaviour (SAB2016), Aberystwyth, UK (2016)Google Scholar
  19. 19.
    Howard, I.S., Wolpert, D.M., Franklin, D.W.: The effect of contextual cues on the encoding of motor memories. J. Neurophysiol. 109(10), 2632–2644 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Martin F. Stoelen
    • 1
    Email author
  • Davide Marocco
    • 1
  • Fabio Bonsignorio
    • 2
    • 3
  • Angelo Cangelosi
    • 4
  1. 1.University of PlymouthPlymouthUK
  2. 2.Institute of BioroboticsScuola Superiore Sant’AnnaPisaItaly
  3. 3.Heron RobotsGenoaItaly
  4. 4.University of ManchesterManchesterUK

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