By Henrik I. Christensen, Aaron Sloman, Geert-Jan Kruijff, Jeremy L. Wyatt (auth.), Henrik Iskov Christensen, Geert-Jan M. Kruijff, Jeremy L. Wyatt (eds.)
Design of cognitive platforms for counsel to humans poses an immense problem to the fields of robotics and synthetic intelligence. The Cognitive platforms for Cognitive information (CoSy) undertaking used to be equipped to handle the problems of i) theoretical growth on layout of cognitive structures ii) tools for implementation of structures and iii) empirical stories to additional comprehend the use and interplay with such platforms. to check, layout and installation cognitive structures there's a have to considers points of platforms layout, embodiment, conception, making plans and blunder restoration, spatial insertion, wisdom acquisition and laptop studying, conversation layout and human robotic interplay and platforms integration. The comfortable undertaking addressed all of those features over a interval of 4 years and throughout various domain names of program – exploration of house and activity / wisdom acquisition for manipulation. the current quantity records the result of the comfortable venture. The comfortable undertaking used to be funded by way of the eu fee as a part of the Cognitive structures application in the sixth Framework Program.
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Extra info for Cognitive Systems
I) direct mapping of space based on a sensorimotor map, ii) encoding of space as a hybrid topological, geometric/semantic map that facilitates reasoning. Both types of representation carry diﬀerent types of advantages, which is the reason why both approaches have been pursued as part of the research in Cosy. e. the type of interaction and operations that an object facilitates. Acquisition of such maps can be acquired through direct interaction with the objects through which the perception-action coupling can be determined and invariances that allow RE-cognition can be identiﬁed.
Context. Another issue, which has been addressed in the project, is contextual information. Traditional object recognition algorithms can, in general, recognize only isolated objects. Some newer object recognition algorithms are robust and enable recognition of objects in the presence of occlusions and cluttered background without explicit ﬁgure-ground segmentation. However, all these algorithms tend to discard background regions and use only the information contained in the foreground. However, the background (contextual) information can be very useful: objects usually appear in a speciﬁc environment (context).
For example, moving the ﬁngertip in a straight line is something that can be described in a way that does not depend on the sensory or motor code that is used by the agent [91, 98, 99]. Multi-modal Learning of Object Categories Representations. Most of the state-of-the-art algorithms for visual learning and recognition represent objects and object categories as collections (or constellations) of descriptors or parts, where each part has its distinctive appearance and spatial position relative to the other parts [141, 139, 111, 79, 80, 63, 2, 42, 73].