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Eye-Head Stabilization Mechanism for a Humanoid Robot Tested on Human Inertial Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9793)

Abstract

Two main classes of reflexes relying on the vestibular system are involved in the stabilization of the human gaze: the vestibulocollic reflex (VCR), which stabilizes the head in space and the vestibulo-ocular reflex (VOR), which stabilizes the visual axis to minimize retinal image motion. Together they keep the image stationary on the retina.

In this work we present the first complete model of eye-head stabilization based on the coordination of VCR and VOR. The model is provided with learning and adaptation capabilities based on internal models. Tests on a simulated humanoid platform replicating torso disturbance acquired on human subject performing various locomotion tasks confirm the effectiveness of our approach.

Keywords

Head stabilization VOR VCR Eye-head coordination Humanoid robotics 

Notes

Acknowledgment

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project). The authors would like to thank the Italian Ministry of Foreign Affairs, General Directorate for the Promotion of the “Country System”, Bilateral and Multilateral Scientific and Technological Cooperation Unit, for the support through the Joint Laboratory on Biorobotics Engineering project.

References

  1. 1.
    Imai, T., Moore, S.T., Raphan, T., Cohen, B.: Interaction of the body, head, and eyes during walking and turning. Exp. Brain Res. 136(1), 1–18 (2001)CrossRefGoogle Scholar
  2. 2.
    Hirasaki, E., Moore, S.T., Raphan, T., Cohen, B.: Effects of walking velocity on vertical head and body movements during locomotion. Exp. Brain Res. 127(2), 117–130 (1999)CrossRefGoogle Scholar
  3. 3.
    Pozzo, T., Berthoz, A., Lefort, L., Vitte, E.: Head stabilization during various locomotor tasks in humans. Exp. Brain Res. 85(1), 208–217 (1991)CrossRefGoogle Scholar
  4. 4.
    Nadeau, S., Amblard, B., Mesure, S., Bourbonnais, D.: Head and trunk stabilization strategies during forward and backward walking in healthy adults. Gait Posture 18(3), 134–142 (2003)CrossRefGoogle Scholar
  5. 5.
    Hashimoto, K., Kang, H.J., Nakamura, M., Falotico, E., Lim, H.O., Takanishi, A., Laschi, C., Dario, P., Berthoz, A.: Realization of biped walking on soft ground with stabilization control based on gait analysis. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2064–2069 (2012)Google Scholar
  6. 6.
    Kang, H.J., Hashimoto, K., Nishikawa, K., Falotico, E., Lim, H.O., Takanishi, A., Laschi, C., Dario, P., Berthoz, A.: Biped walking stabilization on soft ground based on gait analysis. In: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 669–674 (2012)Google Scholar
  7. 7.
    Barnes, G.: Visual-vestibular interaction in the control of head and eye movement: the role of visual feedback and predictive mechanisms. Prog. Neurobiol. 41(4), 435–472 (1993)CrossRefGoogle Scholar
  8. 8.
    Shibata, T., Schaal, S.: Biomimetic gaze stabilization based on feedback-error-learning with nonparametric regression networks. Neural Netw. 14(2), 201–216 (2001)CrossRefGoogle Scholar
  9. 9.
    Viollet, S., Franceschini, N.: A high speed gaze control system based on the vestibulo-ocular reflex. Robot. Auton. Syst. 50(4), 147–161 (2005)CrossRefGoogle Scholar
  10. 10.
    Porrill, J., Dean, P., Stone, J.V.: Recurrent cerebellar architecture solves the motor-error problem. Proc. Roy. Soc. Lond. B 271(1541), 789–796 (2004)CrossRefGoogle Scholar
  11. 11.
    Franchi, E., Falotico, E., Zambrano, D., Muscolo, G., Marazzato, L., Dario, P., Laschi, C.: A comparison between two bio-inspired adaptive models of vestibulo-ocular reflex (VOR) implemented on the iCub robot. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2010, pp. 251–256 (2010)Google Scholar
  12. 12.
    Gay, S., Santos-Victor, J., Ijspeert, A.: Learning robot gait stability using neural networks as sensory feedback function for central pattern generators. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 194–201, November 2013Google Scholar
  13. 13.
    Kryczka, P., Falotico, E., Hashimoto, K., Lim, H., Takanishi, A., Laschi, C., Dario, P., Berthoz, A.: Implementation of a human model for head stabilization on a humanoid platform. In: Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 675–680 (2012)Google Scholar
  14. 14.
    Kryczka, P., Falotico, E., Hashimoto, K., Lim, H.O., Takanishi, A., Laschi, C., Dario, P., Berthoz, A.: A robotic implementation of a bio-inspired head motion stabilization model on a humanoid platform. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2076–2081 (2012)Google Scholar
  15. 15.
    Falotico, E., Cauli, N., Hashimoto, K., Kryczka, P., Takanishi, A., Dario, P., Berthoz, A., Laschi, C.: Head stabilization based on a feedback error learning in a humanoid robot. In: Proceedings - IEEE International Workshop on Robot and Human Interactive Communication, pp. 449–454 (2012)Google Scholar
  16. 16.
    Falotico, E., Laschi, C., Dario, P., Bernardin, D., Berthoz, A.: Using trunk compensation to model head stabilization during locomotion. In: IEEE-RAS International Conference on Humanoid Robots, pp. 440–445 (2011)Google Scholar
  17. 17.
    Vijayakumar, S., Schaal, S.: Locally weighted projection regression: incremental real time learning in high dimensional space. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, pp. 1079–1086 (2000)Google Scholar
  18. 18.
    Tolu, S., Vanegas, M., Luque, N.R., Garrido, J.A., Ros, E.: Bio-inspired adaptive feedback error learning architecture for motor control. Biol. Cybern. 106(8–9), 507–522 (2012)CrossRefGoogle Scholar
  19. 19.
    Tolu, S., Vanegas, M., Garrido, J.A., Luque, N.R., Ros, E.: Adaptive and predictive control of a simulated robot arm. Int. J. Neural Syst. 23(3), 1350010 (2013)CrossRefGoogle Scholar
  20. 20.
    Bergamini, E., Ligorio, G., Summa, A., Vannozzi, G., Cappozzo, A., Sabatini, A.: Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sens. (Switz.) 14(10), 18625–18649 (2014)CrossRefGoogle Scholar
  21. 21.
    Tikhanoff, V., Cangelosi, A., Fitzpatrick, P., Metta, G., Natale, L., Nori, F.: An open-source simulator for cognitive robotics research: the prototype of the icub humanoid robot simulator. In: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, PerMIS 2008, pp. 57–61. ACM, New York (2008)Google Scholar
  22. 22.
    Collewijn, H., Martins, A., Steinman, R.: Natural retinal image motion: origin and change. Ann. N. Y. Acad. Sci. 374(1), 312–329 (1981)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The BioRobotics Institute, Scuola Superiore Sant’AnnaPontederaItaly
  2. 2.Department of Electrical Engineering, The Center for PlaywareTechnical University of DenmarkKongens Lyngby, CopenhagenDenmark

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