Gaze allocation analysis for a visually guided manipulation task

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7426 LNAI, Issue , 2012, Pages 44-53

  Nunez Varela, Jose ^a   Ravindran, Balaraman ^b   Wyatt, Jeremy L ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^b INDIAN INSTITUTE OF TECHNOLOGY MADRAS   (India)

Author keywords

decision making; Gaze control; reinforcement learning

Indexed keywords

ENVIRONMENTAL CONDITIONS; FIELD OF VIEWS; GAZE CONTROL; HUMANOID ROBOT; MANIPULATION TASK; OBSERVATION NOISE; TASK PERFORMANCE; TASK RELEVANT;

ANTHROPOMORPHIC ROBOTS; DECISION MAKING; EYE MOVEMENTS; REINFORCEMENT LEARNING;

COMPUTER SIMULATION;

EID: 84866010549     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33093-3_5     Document Type: Conference Paper

References (16)

1. Cassandra, A.R.: Exact and approximate algorithms for partially observable Markov decision processes. Ph.D. thesis, Brown University (1998)
2. 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)
3. 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)
4. 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)
5. Karaoguz, C., et al.: Optimisation of gaze movements for multitasking using rewards. In: IEEE/RSJ IROS, pp. 1187-1193. IEEE Press (2011)
6. Bradtke, S., Duff, M.: Reinforcement learning methods for continuous-time Markov decision problems. Adv. in Neural Inf. Proc. Sys. 8, 393-400 (1995)
7. Sutton, R., et al.: Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. AI Journal 112(1), 181-211 (1999)
8. Schultz, W.: Multiple reward signals in the brain. Nat. Rev. Neurosci. 1(3), 199-207 (2000)
9. Johansson, R., et al.: Eye-hand coordination in object manipulation. Journal of Neuroscience 21(17), 6917-6932 (2001)
10. Land, M.: Eye movements and the control of actions in everyday life. Progress in Retinal and Eye Research 25(3), 296-324 (2006)
11. Sprague, N., et al.: Modeling embodied visual behaviors. ACM Trans. Appl. Percept. 4(2) (2007)
12. Hayhoe, M., Rothkopf, C.: Vision in the natural world. Wiley Inter. Reviews: Cognitive Science 2(2), 158-166 (2010)
13. Puterman, M.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley-Interscience, New York (1994)
14. Sutton, R., Barto, A.: Introduction to Reinforcement Learning. MIT Press (1998)
15. Thrun, S., et al.: Probabilistic Robotics. MIT Press, Cambridge (2008)
16. Frintrop, S.: VOCUS: A Visual Attention System for Object Detection and Goal-Directed Search. Springer, New York (2006)