Abstract
Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. We have designed and implemented a system for dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task. During the observation of a partner’s reaching movement, the robot is able to contextually estimate the goal position of the partner hand and the location in space of the candidate targets, while moving its gaze around with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control provides a relevant advantage with respect to typical passive observation, both in term of estimation precision and of time required for action recognition.
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References
Bajcsy, R.: Active perception. Proceedings of the IEEE 76(8), 966–1005 (1988)
Ballard, D.H.: Animate vision. AI 48, 57–86 (1991)
de Croon, G.C.H.E., Postma, E.O., van den Herik, H.J.: Adaptive gaze control for object detection. Cognitive Computation 3(1), 264–278 (2011)
Demiris, Y., Khadhouri, B.: Hierarchical attentive multiple models for execution and recognition of actions. Robotics and Autonomous Systems 54, 361–369 (2006)
Demiris, Y., Khadhouri, B.: Content-based control of goal-directed attention during human action perception. Journal of Interaction Studies 9(2), 353–376 (2008)
Demiris, Y., Simmons, G.: Perceiving the unusual: temporal properties of hierarchical motor representations for action perception. Neural Networks 19(3), 272–284 (2006)
Heisz, J.J., Shore, D.I.: More efficient scanning for familiar faces. J. Vis. 8(1), 1–10 (2008)
Kastella, K.: Discrimination gain to optimize detection and classification. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 27(1), 112–116 (1997)
Kwok, C., Fox, D.: Reinforcement learning for sensing strategies. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004 (2004)
Land, M.F.: Eye movements and the control of actions in everyday life. Prog. Retin. Eye Res. 25(3), 296–324 (2006)
Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, New York (1982)
Ognibene, D., Balkenius, C., Baldassarre, G.: Integrating epistemic action (active vision) and pragmatic action (reaching): A neural architecture for camera-arm robots. In: Proceedings of the Tenth International Conference on the Simulation of Adaptive Behavior (2008)
Ognibene, D., Wu, Y., Lee, K., Demiris, Y.: Hierarchies for embodied action perception. Under review (2012)
Ognibene, D., Pezzulo, G., Baldassarre, G.: How can bottom-up information shape learning of top-down attention control skills? In: Proceedings of 9th International Conference on Development and Learning (2010)
Sailer, U., Flanagan, J.R., Johansson, R.S.: Eye-hand coordination during learning of a novel visuomotor task. J. Neurosci. 25(39), 8833–8842 (2005)
Sarabia, M., Ros, R., Demiris, Y.: Towards an open-source social middleware for humanoid robots. In: Proc. 11th IEEE-RAS Int Humanoid Robots (Humanoids) Conf., pp. 670–675 (2011)
Schmidhuber, J., Huber, R.: Learning to generate artificial fovea trajectories for target detection. Int. J. Neural Syst. 2(1-2), 135–141 (1991)
Sommerlade, E., Reid, I.: Information theoretic active scene exploration. In: Proc. IEEE Computer Vision and Pattern Recognition (CVPR) (May 2008)
Suzuki, M., Floreano, D.: Enactive robot vision. Adapt. Behav. 16(2-3), 122–128 (2008)
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Ognibene, D., Chinellato, E., Sarabia, M., Demiris, Y. (2012). Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention. In: Prescott, T.J., Lepora, N.F., Mura, A., Verschure, P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2012. Lecture Notes in Computer Science(), vol 7375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31525-1_17
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DOI: https://doi.org/10.1007/978-3-642-31525-1_17
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