Biomimetic Oculomotor Control

Oculomotor control in a humanoid robot faces similar problems as biological oculomotor systems, that is, capturing targets accurately on a very narrow fovea, dealing with large delays in the control systems, the stabilization of gaze in the face of unknown perturbations of the body, selective attention, and the complexity of stereo vision. In this article, we suggest control circuits to realize three of the most basic oculomotor behaviors and their integration: the vestibulo-ocular reflex and optokinetic response (VOR–OKR) for gaze stabilization, smooth pursuit for tracking moving objects, and saccades for overt visual attention. Each of these behaviors and the mechanism for their integration were derived with inspiration from computational theories as well as behavioral and physiological data in neuroscience. Our implementations on humanoid demonstrate good performance of oculomotor behaviors, which proves to be a viable strategy to explore novel control mechanisms for humanoid robotics. Conversely, insights gained from our models have been able to directly influence views and provide new directions for computational neuroscience research.

[1]  Lawrence Stark,et al.  Predictive Control of Eye Tracking Movements , 1962 .

[2]  P. Dallos,et al.  Learning behavior of the eye fixation control system , 1963 .

[3]  J. Simpson,et al.  Climbing fiber responses evoked in vestibulocerebellum of rabbit from visual system. , 1973, Journal of neurophysiology.

[4]  R. Carpenter,et al.  Movements of the Eyes , 1978 .

[5]  A. Liegeois,et al.  Automatic supervisory control of the configuration and behavior of multi-body mechanisms , 1977 .

[6]  D A Robinson,et al.  The use of control systems analysis in the neurophysiology of eye movements. , 1981, Annual review of neuroscience.

[7]  S. Whittaker,et al.  Learning patterns of eye motion for foveal pursuit. , 1982, Investigative ophthalmology & visual science.

[8]  A. Holden.  Competition and cooperation in neural nets , 1983 .

[9]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Christof Koch,et al.  Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention , 1984 .

[11]  E. Watanabe.  Role of the primate flocculus in adaptation of the vestibulo-ocular reflex , 1985, Neuroscience Research.

[12]  P. Kumar,et al.  Theory and practice of recursive identification , 1985, IEEE Transactions on Automatic Control.

[13]  E. J. Morris,et al.  Visual motion processing and sensory-motor integration for smooth pursuit eye movements. , 1987, Annual review of neuroscience.

[14]  R. Bajcsy.  Active perception , 1988, Proc. IEEE.

[15]  Mitsuo Kawato,et al.  Feedback-Error-Learning Neural Network for Supervised Motor Learning , 1990 .

[16]  R. Eckmiller.  Advanced neural computers , 1990 .

[17]  M. Ito.  A new physiological concept on cerebellum. , 1990, Revue neurologique.

[18]  Christopher Brown,et al.  Gaze controls with interactions and decays , 1989, IEEE Trans. Syst. Man Cybern..

[19]  David W. Murray,et al.  Gaze Control for a Two-Eyed Robot Head , 1991 .

[20]  S. Zeki.  The visual image in mind and brain. , 1992, Scientific American.

[21]  Dana H. Ballard,et al.  Principles of animate vision , 1992, CVGIP Image Underst..

[22]  G. Barnes,et al.  Visual-vestibular interaction in the control of head and eye movement: The role of visual feedback and predictive mechanisms , 1993, Progress in neurobiology.

[23]  Rajesh P. N. Rao,et al.  Learning Saccadic Eye Movements Using Multiscale Spatial Filters , 1994, NIPS.

[24]  Atsuo Takanishi,et al.  Development of an anthropomorphic head-eye system for robot and human communication , 1995, Proceedings 4th IEEE International Workshop on Robot and Human Communication.

[25]  Ian Reid,et al.  Active exploration of dynamic and static scenes , 1995 .

[26]  Demetri Terzopoulos,et al.  Real-time computer vision , 1995 .

[27]  S. Grossberg.  The Attentive Brain , 1995 .

[28]  Ian D. Reid,et al.  The Active Recovery of 3D Motion Trajectories and Their Use in Prediction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  Michael Hansen,et al.  Biologically inspired calibration-free adaptive saccade control of a binocular camera-head , 1997, Biological Cybernetics.

[30]  Atsuo Takanishi,et al.  Development of an anthropomorphic head-eye robot with two eyes-coordinated head-eye motion and pursuing motion in the depth direction , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[31]  Joseph A. Driscoll,et al.  A visual attention network for a humanoid robot , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[32]  Yasuo Kuniyoshi,et al.  Neural learning of embodied interaction dynamics , 1998, Neural Networks.

[33]  Mitsuo Kawato,et al.  Internal models for motor control and trajectory planning , 1999, Current Opinion in Neurobiology.

[34]  Brian Scassellati,et al.  A Context-Dependent Attention System for a Social Robot , 1999, IJCAI.

[35]  Christof Koch,et al.  Comparison of feature combination strategies for saliency-based visual attention systems , 1999, Electronic Imaging.

[36]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[37]  Stefan Schaal,et al.  Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space , 2000, ICML.

[38]  Stefan Schaal,et al.  Fast and efficient incremental learning for high-dimensional movement systems , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[39]  Tamar Flash,et al.  Computational approaches to motor control , 2001, Current Opinion in Neurobiology.

[40]  Stefan Schaal,et al.  Biomimetic gaze stabilization based on feedback-error-learning with nonparametric regression networks , 2001, Neural Networks.

[41]  Stefan Schaal,et al.  Statistical Learning for Humanoid Robots , 2002, Auton. Robots.

[42]  F. Panerai.  Adaptive image stabilization : a need for vision-based active robotic agents , 2022 .