Abstract
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.
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Acknowledgments
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.
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Lucas, A., Ricolfe-Viala, C., Di Nuovo, A. (2019). Preliminary Investigation on Visual Finger-Counting with the iCub Robot Cameras and Hands. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11650. Springer, Cham. https://doi.org/10.1007/978-3-030-25332-5_46
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DOI: https://doi.org/10.1007/978-3-030-25332-5_46
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