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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ognibene, D., Balkenius, C., & Baldassarre, G. (2008). Integrating epistemic action (active vision) and pragmatic action (reaching): A neural architecture for camera-arm robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5040 LNAI, pp. 220–229). https://doi.org/10.1007/978-3-540-69134-1_22
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