The ability to perceive and understand our dynamic real world is critical for the next generation of multi-sensory robotic systems. One of the most important tasks is searching, but the biological mechanism is seen as an adaptive process that searches for an object of interest managing the limited available processing. Actually, cognitive robots require attention mechanisms to determine what parts of the sensory array they need to process, in the same way than the biological systems. In other words, attention consists in selecting the most relevant information from multi-sensory inputs to perform efficiently the search of a target. A first mechanism consists in a bottom-up approach that is inherent to the scenario and happens at a very early processing stage. This mechanism also includes some prior symbolic contextual knowledge about the target under consideration. This knowledge determines to a large extent the area that the robot will have to examine first, and it is usually expressed in natural language in the form of resources such as the lexica ontology and could be directly accessible by the robot to narrow down the visual search space.
Then, when the robot decides to examine a specific selected area, the robot should have the model of the target with information about its shape, size, color, or texture. This model should describe the target enough to allow the robot to efficiently find a small number of candidates. This second process is voluntary and is called top-down attention.
By providing multiple relevant contributions across the spectrum of the FP7 objectives in terms of its potential to advance robotic manufacturing, autonomous navigation, and computing paradigms, this project will enable the candidate to maintain and enhance his position at the forefront of advances in this field.
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