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Proceedings Paper

Connecting a cognitive architecture to robotic perception
Author(s): Unmesh Kurup; Christian Lebiere; Anthony Stentz; Martial Hebert
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Paper Abstract

We present an integrated architecture in which perception and cognition interact and provide information to each other leading to improved performance in real-world situations. Our system integrates the Felzenswalb et. al. object-detection algorithm with the ACT-R cognitive architecture. The targeted task is to predict and classify pedestrian behavior in a checkpoint scenario, most specifically to discriminate between normal versus checkpoint-avoiding behavior. The Felzenswalb algorithm is a learning-based algorithm for detecting and localizing objects in images. ACT-R is a cognitive architecture that has been successfully used to model human cognition with a high degree of fidelity on tasks ranging from basic decision-making to the control of complex systems such as driving or air traffic control. The Felzenswalb algorithm detects pedestrians in the image and provides ACT-R a set of features based primarily on their locations. ACT-R uses its pattern-matching capabilities, specifically its partial-matching and blending mechanisms, to track objects across multiple images and classify their behavior based on the sequence of observed features. ACT-R also provides feedback to the Felzenswalb algorithm in the form of expected object locations that allow the algorithm to eliminate false-positives and improve its overall performance. This capability is an instance of the benefits pursued in developing a richer interaction between bottom-up perceptual processes and top-down goal-directed cognition. We trained the system on individual behaviors (only one person in the scene) and evaluated its performance across single and multiple behavior sets.

Paper Details

Date Published: 25 May 2012
PDF: 8 pages
Proc. SPIE 8387, Unmanned Systems Technology XIV, 83870X (25 May 2012); doi: 10.1117/12.919417
Show Author Affiliations
Unmesh Kurup, Carnegie Mellon Univ. (United States)
Christian Lebiere, Carnegie Mellon Univ. (United States)
Anthony Stentz, Carnegie Mellon Univ. (United States)
Martial Hebert, Carnegie Mellon Univ. (United States)


Published in SPIE Proceedings Vol. 8387:
Unmanned Systems Technology XIV
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Grant R. Gerhart, Editor(s)

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