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A COSPAL Subsystem: Solving a Shape-Sorter Puzzle
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.ORCID iD: 0000-0002-6096-3648
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.ORCID iD: 0000-0002-5698-5983
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
Linköping University, The Institute of Technology. Linköping University, Department of Electrical Engineering, Computer Vision.
2005 (English)In: AAAI Fall Symposium: From Reactive to Anticipatory Cognitive Embedded Systems, FS-05-05, AAAI Press , 2005, 65-69 p.Conference paper, Published paper (Refereed)
Abstract [en]

 To program a robot to solve a simple shape-sorter puzzle is trivial. To devise a Cognitive System Architecture, which allows the system to find out by itself how to go about a solution, is less than trivial. The development of such an architecture is one of the aims of the COSPAL project, leading to new techniques in vision based Artificial Cognitive Systems, which allow the development of robust systems for real dynamic environments. The systems developed under the project itself remain however in simplified scenarios, likewise the shape-sorter problem described in the present paper. The key property of the described system is its robustness. Since we apply association strategies of local features, the system behaves robustly under a wide range of distortions, as occlusion, colour and intensity changes. The segmentation step which is applied in many systems known from literature is replaced with local associations and view-based hypothesis validation. The hypotheses used in our system are based on the anticipated state of the visual percepts. This state replaces explicit modeling of shapes. The current state is chosen by a voting system and verified against the true visual percepts. The anticipated state is obtained from the association to the manipulator actions, where reinforcement learning replaces the explicit calculation of actions. These three differences to classical schemes allow the design of a much more generic and flexible system with a high level of robustness. On the technical side, the channel representation of information and associative learning in terms of the channel learning architecture are essential ingredients for the system. It is the properties of locality, smoothness, and non-negativity which make these techniques suitable for this kind of application. The paper gives brief descriptions of how different system parts have been implemented and show some examples from our tests.

Place, publisher, year, edition, pages
AAAI Press , 2005. 65-69 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-37187Local ID: 33895OAI: oai:DiVA.org:liu-37187DiVA: diva2:258036
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2016-05-04

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Felsberg, MichaelForssén, Per-ErikMoe, AndersGranlund, Gösta

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