Internal rehearsal for a cognitive robot using collision detection

Abstract

Internal rehearsal is a relatively new research area allowing a robot to contemplate the consequences of its actions before they are attempted by the actuation system. This thesis focuses on integrating internal rehearsal into the area of collision detection. This thesis was inspired by Murray Shanahan’s work involving brain-based robotic internal simulation. This research explores how internal rehearsal, by using a collision detection method via collision spheres, may enhance task execution. When the cognitive robot ISAC performs a task involving percepts and arm behaviors, the Internal Rehearsal System (IRS), with a help from the Central Executive Agent, performs this task internally and determines if a collision occurs. The CEA Internal rehearsal covered in this thesis involves a pair of one percept (stored in the short-term memory) and one arm behavior (stored in the long-term memory and recalled into the working memory) with two objects perceived on the robot’s Sensory Egosphere. If an undesirable collision occurs within the IRS, the robot will try a different behavior to avoid this predicted collision.