Brain-inspired Sensorimotor Robotic Platform - Learning in Cerebellum-driven Movement Tasks through a Cerebellar Realistic Model

Claudia Casellato, Jesus A. Garrido, Cristina Franchin, Giancarlo Ferrigno, Egidio D'Angelo, Alessandra Pedrocchi

2013

Abstract

Biologically inspired neural mechanisms, coupling internal models and adaptive modules, can be an effective way of constructing a control system that exhibits a human-like behaviour. A brain-inspired controller has been developed, embedding a cerebellum-like adaptive module based on neurophysiological plasticity mechanisms. It has been tested as controller of an ad-hoc developed neurorobot, integrating a 3 degrees of freedom serial robotic arm with a motion tracking system. The learning skills have been tried out, designing a vestibular-ocular reflex (VOR) protocol. One robot joint was used to get the desired head turn, while another joint displacement corresponded to the eye motion, which was controlled by the cerebellar model output, used as joint torque. Along task repetitions, the cerebellum was able to produce an anticipatory eye displacement, which accurately compensated the head turn in order to keep on fixing the environmental object. Multiple tests have been implemented, pairing different head turn with object motion. The gaze error and the cerebellum output were quantified. The VOR was accurately tuned thanks to the cerebellum plasticity. The next steps will include the activation of multiple plasticity sites evaluating the real platform behaviour in different sensorimotor tasks.

References

  1. Albus, J.S. (1971). A Theory of Cerebellar Function. Math Biosci., 10, 25-61.
  2. Boyden, E.S., Katoh, A., Raymond, J.L. (2004). Cerebellum-dependant Learning: The Role of Multiple Plasticity Mechanisms. Annu Rev Neurosci., 27, 581- 609.
  3. Burdess, C. (1996). The Vestibulo-Ocular Reflex: Computation in The Cerebellar Flocculus. Retrieved (n.d.) from hxxp://bluezoo.org/vor/vor.pdf.
  4. Casellato, C., Pedrocchi, A., Garrido, J.A., Luque, N.R., Ferrigno, G., D'Angelo, E., Ros, A. (2012). An Integrated Motor Control Loop of a Human-like Robotic Arm: Feedforward, Feedback and Cerebellum-based Learning. Biomedical Robotics and Biomechatronics (BioRob), 2012 June 24-27 4th IEEE RAS & EMBS International Conference, no., 562-567. doi: 10.1109/BioRob.2012.6290791.
  5. Donchin, O., Rabe, K., Diedrichsen, J., Schoch, B., Gizewski, E.R., Timmann, D. (2012). Cerebellar Regions Involved in Adaptation to Force Field and Visuomotor Perturbation. J Neurophysiol., 107, 134- 147.
  6. Gao, Z., van Beugen, B.J., De Zeeuw, C.I. (2012). Distributed Synergistic Plasticity and Cerebellar Learning. Nat Rev Neurosci., 13, 619-35.
  7. Hwang, E.J., Shadmehr, R.J. (2005). Internal Models of Limb Dynamics and The Encoding of Limb State. Neural Eng., 2(3), 266-78.
  8. Hoffland, B.S., Bologna, M., Kassavetis, P., Teo, J.T.H., Rothwell, J.C., Yeo, C.H., van de Warrenburg, B.P., Edwards, M.J. (2012). Cerebellar Theta Burst Stimulation Impairs Eyeblink Classical Conditioning. J Physiol., 590(4), 887-897.
  9. Ito M. (2006). Cerebellar Circuitry as a Neuronal Machine. Prog Neurobiol., 78, 272-303.
  10. Luque, N.R., Garrido, J.A., Carrillo, R.R., Coenen, O.J., Ros, E. (2011). Cerebellar Input Configuration Toward Object Model Abstraction in Manipulation Tasks. Neural Networks, IEEE Transactions on, 22(8), 1321-1328.
  11. Marr, D. (1969). A Theory of Cerebellar Cortex. J Physiol., 202(2), 437-470.
  12. van der Smagt, P. (2000). Benchmarking Cerebellar Control. Robotics and Autonomous Systems, 32, 237- 251.
  13. Yamamoto, K., Kawato, M., Kotosaka, S., Kitazawa, S. (2007). Encoding of Movement Dynamics by Purkinje Cell Simple Spike Activity during Fast Arm Movements under Resistive and Assistive Force Field. J Neurophysiol., 97(2), 1588-99.
  14. Yamazaki, T., Tanaka, S. (2007). The cerebellum as a liquid state machine. Neural Networks, 20(3), 290- 297.
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Paper Citation


in Harvard Style

Casellato C., A. Garrido J., Franchin C., Ferrigno G., D'Angelo E. and Pedrocchi A. (2013). Brain-inspired Sensorimotor Robotic Platform - Learning in Cerebellum-driven Movement Tasks through a Cerebellar Realistic Model . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 568-573. DOI: 10.5220/0004659305680573


in Bibtex Style

@conference{sscn13,
author={Claudia Casellato and Jesus A. Garrido and Cristina Franchin and Giancarlo Ferrigno and Egidio D'Angelo and Alessandra Pedrocchi},
title={Brain-inspired Sensorimotor Robotic Platform - Learning in Cerebellum-driven Movement Tasks through a Cerebellar Realistic Model},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2013)},
year={2013},
pages={568-573},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004659305680573},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2013)
TI - Brain-inspired Sensorimotor Robotic Platform - Learning in Cerebellum-driven Movement Tasks through a Cerebellar Realistic Model
SN - 978-989-8565-77-8
AU - Casellato C.
AU - A. Garrido J.
AU - Franchin C.
AU - Ferrigno G.
AU - D'Angelo E.
AU - Pedrocchi A.
PY - 2013
SP - 568
EP - 573
DO - 10.5220/0004659305680573