Abstract
In order to follow a moving visual target, humans generate voluntary smooth pursuit eye movements. The purpose of smooth pursuit eye movements is to minimize the retinal slip, i.e. the target velocity projected onto the retina. In this paper we propose a model able to integrate the major characteristics of visually guided and predictive control of the smooth pursuit. The model is composed of an Inverse Dynamics Controller (IDC) for the feedback control, a neural predictor for the anticipation of the target motion and a Weighted Sum module that is able to combine the previous systems in a proper way. In order to validate the general model, two implementations with two different IDC controllers have been carried out. The first one uses a backstepping-based controller to generate velocity motor commands for the eye movements and the other one uses a bio-inspired neurocontroller to generate position motor commands for eye-neck coordinated movements. Our results, tested on the iCub robot simulator, show that both implementations can use prediction for a zero-lag visual tracking, a feedback based control for “unpredictable” target pursuit and can combine these two approaches by properly switching from one to the other, guaranteeing a stable visual pursuit.
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References
Fukushima, J., Morita, N., Fukushima, K., Chiba, T., Tanaka, S., Yamashita, I.: Voluntary control of saccadic eye movements in patients with schizophrenic and affective disorders. Journal of Psychiatric Research 24(1), 9–24 (1990)
Robinson, D.A., Gordon, J., Gordon, S.: A model of the smooth pursuit eye movement system. Biological Cybernetics 55(1), 43–57 (1986)
Krauzlis, R.J., Lisberger, S.G.: A model of visually-guided smooth pursuit eye movements based on behavioral observations. Journal of Computational Neuroscience 1(4), 265–283 (1994)
Shibata, T., Tabata, H., Schaal, S., Kawato, M.: A model of smooth pursuit in primates based on learning the target dynamics. Neural Networks 18(3), 213–224 (2005)
Zambrano, D., Falotico, E., Manfredi, L., Laschi, C.: A model of the smooth pursuit eye movement with prediction and learning. Applied Bionics and Biomechanics 7(2), 109–118 (2010)
de Xivry, J.J.O., Coppe, S., Blohm, G., Lefevre, P.: Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics. The Journal of Neuroscience 33(44), 17301–17313 (2013)
Shibata, T., Vijayakumar, S., Conradt, J., Schaal, S.: Biomimetic oculomotor control. Adaptive Behavior 9(3–4), 189–207 (2001)
Falotico, E., Zambrano, D., Muscolo, G.G., Marazzato, L., Dario, P., Laschi, C.: Implementation of a bio-inspired visual tracking model on the icub robot. In: Proc. 19th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2010), pp. 564–569. IEEE (2010)
Falotico, E., Taiana, M., Zambrano, D., Bernardino, A., Santos-Victor, J., Dario, P., Laschi, C.: Predictive tracking across occlusions in the icub robot. In: Proceedings of the 9th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2009), pp. 486–491, December 2009
Vannucci, L., Cauli, N., Falotico, E., Bernardino, A., Laschi, C.: Adaptive visual pursuit involving eye-head coordination and prediction of the target motion. In: Proceedings of the 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2014), pp. 541–546. IEEE (2014)
Dario, P., Carrozza, M.C., Guglielmelli, E., Laschi, C., Menciassi, A., Micera, S., Vecchi, F.: Robotics as a future and emerging technology: biomimetics, cybernetics, and neuro-robotics in european projects. IEEE Robotics & Automation Magazine 12(2), 29–45 (2005)
Viollet, S., Franceschini, N.: A high speed gaze control system based on the vestibulo-ocular reflex. Robotics and Autonomous systems 50(4), 147–161 (2005)
Lenz, A., Balakrishnan, T., Pipe, A.G., Melhuish, C.: An adaptive gaze stabilization controller inspired by the vestibulo-ocular reflex. Bioinspiration & Biomimetics 3(3), 035001 (2008)
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: Proceedings of the 10th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2010), pp. 251–256, December 2010
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 65(6), 386 (1958)
Weigend, A.S., Huberman, B.A., Rumelhart, D.E.: Predicting the future: A connectionist approach. International Journal of Neural Systems 1(03), 193–209 (1990)
Widrow, B., Hoff, M.E.: Adaptive switching circuits (1960)
Beira, R., Lopes, M., Praga, M., Santos-Victor, J., Bernardino, A., Metta, G., Becchi, F., Saltarén, R.: Design of the robot-cub (icub) head. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation (ICRA 2006), pp. 94–100. IEEE (2006)
Kokotovie, P.V.: The joy of feedback: nonlinear and adaptive. IEEE Control Systems Magazine 12(3), 7–17 (1992)
Janabi-Sharifi, F., Hayward, V., Chen, C.S.: Discrete-time adaptive windowing for velocity estimation. IEEE Transactions on Control Systems Technology 8(6), 1003–1009 (2000)
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Vannucci, L., Falotico, E., Di Lecce, N., Dario, P., Laschi, C. (2015). Integrating Feedback and Predictive Control in a Bio-Inspired Model of Visual Pursuit Implemented on a Humanoid Robot. In: Wilson, S., Verschure, P., Mura, A., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2015. Lecture Notes in Computer Science(), vol 9222. Springer, Cham. https://doi.org/10.1007/978-3-319-22979-9_26
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