Abstract
Findings from eye movement research in humans have demonstrated that the task determines where to look. One hypothesis is that the purpose of looking is to reduce uncertainty about properties relevant to the task. Following this hypothesis, we define a model that poses the problem of where to look as one of maximising task performance by reducing task relevant uncertainty. We implement and test our model on a simulated humanoid robot which has to move objects from a table into containers. Our model outperforms and is more robust than two other baseline schemes in terms of task performance whilst varying three environmental conditions, reach/grasp sensitivity, observation noise and the camera’s field of view.
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References
Cassandra, A.R.: Exact and approximate algorithms for partially observable Markov decision processes. Ph.D. thesis, Brown University (1998)
Metta, G., et al.: The iCub humanoid robot: An open platform for research in embodied cognition. In: Proc. ACM Perf. Metrics for Int. Sys., pp. 50–56. ACM, New York (2008)
Pattacini, U., et al.: An experimental evaluation of a novel minimum-jerk cartesian controller for humanoid robots. In: IEEE IROS, pp. 1668–1674. IEEE Press (2010)
Nunez-Varela, J., et al.: Where do I look now? Gaze allocation during visually guided manipulation. In: IEEE ICRA, pp. 4444–4449. IEEE Press (2012)
Karaoguz, C., et al.: Optimisation of gaze movements for multitasking using rewards. In: IEEE/RSJ IROS, pp. 1187–1193. IEEE Press (2011)
Bradtke, S., Duff, M.: Reinforcement learning methods for continuous-time Markov decision problems. Adv. in Neural Inf. Proc. Sys. 8, 393–400 (1995)
Sutton, R., et al.: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. AI Journal 112(1), 181–211 (1999)
Schultz, W.: Multiple reward signals in the brain. Nat. Rev. Neurosci. 1(3), 199–207 (2000)
Johansson, R., et al.: Eye-hand coordination in object manipulation. Journal of Neuroscience 21(17), 6917–6932 (2001)
Land, M.: Eye movements and the control of actions in everyday life. Progress in Retinal and Eye Research 25(3), 296–324 (2006)
Sprague, N., et al.: Modeling embodied visual behaviors. ACM Trans. Appl. Percept. 4(2) (2007)
Hayhoe, M., Rothkopf, C.: Vision in the natural world. Wiley Inter. Reviews: Cognitive Science 2(2), 158–166 (2010)
Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley-Interscience, New York (1994)
Sutton, R., Barto, A.: Introduction to Reinforcement Learning. MIT Press (1998)
Thrun, S., et al.: Probabilistic Robotics. MIT Press, Cambridge (2008)
Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. Springer, New York (2006)
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Nunez-Varela, J., Ravindran, B., Wyatt, J.L. (2012). Gaze Allocation Analysis for a Visually Guided Manipulation Task. In: Ziemke, T., Balkenius, C., Hallam, J. (eds) From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science(), vol 7426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33093-3_5
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DOI: https://doi.org/10.1007/978-3-642-33093-3_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33092-6
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