We propose to examine a representation which features combined action and perception signals, i.e., instead of having a purely geometric representation of the perceptual data, we include the motor actions, e.g., aiming a camera at an object, which are also actions that generate the particular shape. This generative perception-action representation uses Leyton’s cognitive representation based on wreath products. The wreath product is a special kind of group which captures information through symmetries on the sensorimotor data. The key insight is the bundling of actuation and perception data together in order to capture the cognitive structure of interactions with the world. This involves developing algorithms and methods: (1) to perform symmetry detection and parsing, (2) to represent and characterize uncertainties in the data and representations, and (3) to provide an overall cognitive architecture for a robot agent. We demonstrate these functions in 2D text classification, as well as on 3D data, on a real robot operating according to a well-defined experimental protocol for benchmarking indoor navigation, along with capabilities for multirobot communication and knowledge sharing. A cognitive architecture called the Wreath Product Cognitive Architecture is developed to support this approach.
|Advisor:||Henderson, Thomas C.|
|Commitee:||Asfour, Tamim, Cohen, Elaine, Fletcher, Preston Thomas, Hansen, Charles D.|
|School:||The University of Utah|
|Department:||School of Computing|
|School Location:||United States -- Utah|
|Source:||DAI-B 78/06(E), Dissertation Abstracts International|
|Subjects:||Robotics, Artificial intelligence, Computer science|
|Keywords:||Cognitive architecture, Cognitive robotics, Computer vision, Generative representation, Symmetry analysis, Wreath product|
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