Abstract
The active vision and attention-for-action frameworks propose that in organisms attention and perception are closely integrated with action and learning. This work proposes a novel bio-inspired integrated neural-network architecture that on one side uses attention to guide and furnish the parameters to action, and on the other side uses the effects of action to train the task-oriented top-down attention components of the system. The architecture is tested both with a simulated and a real camera-arm robot engaged in a reaching task. The results highlight the computational opportunities and difficulties deriving from a close integration of attention, action and learning.
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References
Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, New York (1982)
Fermuller, C., Aloimonos, Y.: Vision and action. Image Vision Comput. 13(10), 725–744 (1995)
Ballard, D.: Animate vision. Artif. Intell. 48, 57–86 (1991)
Posner, M.I.: Orienting of attention. Q J. Exp. Psychol. 32(1), 3–25 (1980)
Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognit Psychol. 12(1), 97–136 (1980)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology. MIT Press, Cambridge (2000)
Floreano, D., Kato, T., Marocco, D., Sauser, E.: Coevolution of active vision and feature selection. Biol. Cybern. 90(3), 218–228 (2004)
Cliff, D., Noble, J.: Knowledge-based vision and simple visual machines. Philos. T Roy Soc. B 352(1358), 1165–1175 (1997)
de Croon, G., Postma, E.: Sensory-motor coordination in object detection. In: IEEE Symp. ALIFE 2007, pp. 147–154 (2007)
Whitehead, S.D., Ballard, D.H.: Learning to perceive and act by trial and error. Mach. Learn. 7(1), 45–83 (1991)
Allport, D.: Selection for action: Some behavioral and neurophysiological considerations of attention and action. In: Perspectives on perception and action, vol. 15, pp. 395–419. Erlbaum, Hillsdale (1987)
Neumann, O.: Direct parameter specification and the concept of perception. Psychol. Res. 52(2-3), 207–215 (1990)
Balkenius, C.: Attention, habituation and conditioning: Toward a computational model. Cogn. Sci.Quart. 1(2), 171–204 (2000)
Itti, L., Koch, C.: Computational modelling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)
Schmidhuber, J., Huber, R.: Learning to generate artificial fovea trajectories for target detection. Int. J. Neural Syst. 2(1-2), 135–141 (1991)
Ognibene, D., Balkenius, C., Baldassarre, G.: A reinforcement-learning model of top-down attention based on a potential-action map. In: The Anticipatory Approach. Springer, Berlin (2008)
Ognibene, D., Rega, A., Baldassarre, G.: A model of reaching that integrates reinforcement learning and population encoding of postures. In: 9th Int. Conf. Simul. Adapt. Behav., September 2006, pp. 381–393. Springer, Heidelberg (2006)
Pouget, A., Ducom, J.C., Torri, J., Bavelier, D.: Multisensory spatial representations in eye-centered coordinates for reaching. Cognition 83(1), B1–11 (2002)
Pouget, A., Zhang, K., Deneve, S., Latham, P.E.: Statistically efficient estimation using population coding. Neural Comput. 10(2), 373–401 (1998)
Cisek, P.: Integrated neural processes for defining potential actions and deciding between them: a computational model. J. Neurosci. 26(38), 9761–9770 (2006)
Erlhagen, W., Schöner, G.: Dynamic field theory of movement preparation. Psychol. Rev. 109(3), 545–572 (2002)
Sutton, R., Barto, A.: Reinforcement Learning. MIT Press, Cambridge (1998)
Dominey, P.F., Arbib, M.A.: A cortico-subcortical model for generation of spatially accurate sequential saccades. Cereb Cortex 2(2), 153–175 (1992)
Klein: Inhibition of return. Trends Cogn. Sci. 4(4), 138–147 (2000)
Herbort, O., Ognibene, D., Butz, M.V., Baldassarre, G.: Learning to select targets within targets in reaching tasks. In: IEEE 6th Intern. Conf. Development Learning, July 2007, pp. 7–12 (2007)
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Ognibene, D., Balkenius, C., Baldassarre, G. (2008). Integrating Epistemic Action (Active Vision) and Pragmatic Action (Reaching): A Neural Architecture for Camera-Arm Robots. In: Asada, M., Hallam, J.C.T., Meyer, JA., Tani, J. (eds) From Animals to Animats 10. SAB 2008. Lecture Notes in Computer Science(), vol 5040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69134-1_22
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DOI: https://doi.org/10.1007/978-3-540-69134-1_22
Publisher Name: Springer, Berlin, Heidelberg
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