Document Type


Date of Original Version



Mechanical, Industrial and Systems Engineering


This paper investigates an approach that uses the cognitive architecture Soar to improve the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from laptop cases. Soar's long-term memory module, semantic memory, was used to remember pieces of information regarding laptop models and screw holes. The system was trained with multiple laptop models and the method in which Soar was used to facilitate the removal of screws was varied to determine the best performance of the system. In all the cases, Soar could determine the correct laptop model and in what orientation it was placed in the system. Soar was also used to remember what circle locations that were explored contained screws and what circles did not. Remembering the locations of the holes decreased a trial time by over 60%. The system performed the best when the number of training trials used to explore circle locations was limited, as this decreased the total trial time by over 10% for most of the laptop models and orientations.

Publication Title, e.g., Journal

IEEE Transactions on Automation Science and Engineering