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Proceedings Paper

Integrating perception and problem solving to predict complex object behaviors
Author(s): Damian M. Lyons; Sirhan Chaudhry; Marius Agica; John Vincent Monaco
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Paper Abstract

One of the objectives of Cognitive Robotics is to construct robot systems that can be directed to achieve realworld goals by high-level directions rather than complex, low-level robot programming. Such a system must have the ability to represent, problem-solve and learn about its environment as well as communicate with other agents. In previous work, we have proposed ADAPT, a Cognitive Architecture that views perception as top-down and goaloriented and part of the problem solving process. Our approach is linked to a SOAR-based problem-solving and learning framework. In this paper, we present an architecture for the perceptive and world modelling components of ADAPT and report on experimental results using this architecture to predict complex object behaviour. A novel aspect of our approach is a 'mirror system' that ensures that the modelled background and foreground objects are synchronized with observations and task-based expectations. This is based on our prior work on comparing real and synthetic images. We show results for a moving object that collides and rebounds from its environment, hence showing that this perception-based problem solving approach has the potential to be used to predict complex object motions.

Paper Details

Date Published: 28 April 2010
PDF: 6 pages
Proc. SPIE 7710, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010, 77100G (28 April 2010); doi: 10.1117/12.852484
Show Author Affiliations
Damian M. Lyons, Fordham Univ. (United States)
Sirhan Chaudhry, Fordham Univ. (United States)
Marius Agica, Pace Univ. (United States)
John Vincent Monaco, Pace Univ. (United States)

Published in SPIE Proceedings Vol. 7710:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2010
Jerome J. Braun, Editor(s)

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