25 May 2012 Connecting a cognitive architecture to robotic perception
Author Affiliations +
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Unmesh Kurup, Unmesh Kurup, Christian Lebiere, Christian Lebiere, Anthony Stentz, Anthony Stentz, Martial Hebert, Martial Hebert, } "Connecting a cognitive architecture to robotic perception", Proc. SPIE 8387, Unmanned Systems Technology XIV, 83870X (25 May 2012); doi: 10.1117/12.919417; https://doi.org/10.1117/12.919417


RCTA capstone assessment
Proceedings of SPIE (May 21 2015)
Assessment of RCTA research
Proceedings of SPIE (May 04 2017)
Toward cognitive robotics
Proceedings of SPIE (April 30 2009)
Integrated system for sensing and traverse of cliff faces
Proceedings of SPIE (September 29 2003)
Multi-agent system for target-adaptive radar tracking
Proceedings of SPIE (May 07 2012)

Back to Top