Preliminary Investigation on Visual Finger-Counting with the iCub Robot Cameras and Hands

  • Alexandr LucasEmail author
  • Carlos Ricolfe-Viala
  • Alessandro Di Nuovo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)


This short paper describes an approach for collecting a dataset of hand’s pictures and training a Deep Learning network that could enable the iCub robot to count on its fingers using solely its own cameras. Such a skill, mimicking children’s habits, can support arithmetic learning in a baby robot, an important step in creating artificial intelligence for robots that could learn like children in the context of cognitive developmental robotics. Preliminary results show the approach is promising in terms of accuracy.


Developmental robotics Finger-counting Faster R-CNN iCub 



This work has been supported by the EPSRC through the grant no. EP/P030033/1 (NUMBERS). Authors are grateful to the NVIDIA Corporation for donating GeForce GTX TITAN X that has been used to accelerate the computation.


  1. 1.
    Asada, M., et al.: Cognitive developmental robotics as a new paradigm for the design of humanoid robots. Robot. Auton. Syst. 37(2–3), 185–193 (2001)CrossRefGoogle Scholar
  2. 2.
    Cangelosi, A., Schlesinger, M.: Developmental Robotics: From Babies to Robots. MIT Press, Cambridge (2015)CrossRefGoogle Scholar
  3. 3.
    Dantzig, T.: Number-The Language of Science. Free Press, New York (1954)zbMATHGoogle Scholar
  4. 4.
    Fischer, M.H., et al.: Finger Counting and Numerical Cognition. Front. Psychol. 3, 108 (2012)Google Scholar
  5. 5.
    Goldin-Meadow, S., et al.: Gesture’s role in learning arithmetic. In: Edwards, L.D., et al. (eds.) Emerging Perspectives on Gesture and Embodiment in Mathematics, pp. 51–72. Information Age Publishing, Charlotte (2014)Google Scholar
  6. 6.
    De La Cruz, V.M. et al.: Making fingers and words count in a cognitive robot. Front. Behav. Neurosci. 8, 12 pages (2014)Google Scholar
  7. 7.
    Leitner, J. et al.: Humanoid learns to detect its own hands. In: 2013 IEEE Congress Evolutionary Computation, CEC 2013, pp. 1411–1418 (2013)Google Scholar
  8. 8.
    Di Nuovo, A., Jay, T.: The development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi-disciplinary research. IET Cogn. Comput. Syst. 1, 2–11 (2019)Google Scholar
  9. 9.
    Soylu, F., et al.: You can count on your fingers: the role of fingers in early mathematical development. J. Numer. Cogn. 4(1), 2363–8761 (2018)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Vicente, P., et al.: Robotic hand pose estimation based on stereo vision and GPU-enabled internal graphical simulation. J. Intell. Robot. Syst. 83(3–4), 339–358 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alexandr Lucas
    • 1
    Email author
  • Carlos Ricolfe-Viala
    • 2
  • Alessandro Di Nuovo
    • 1
  1. 1.Sheffield Hallam UniversitySheffieldUK
  2. 2.Universitat Politecnica de ValenciaValenciaSpain

Personalised recommendations