Applying Ant Colony Optimization algorithms for high-level behavior learning and reproduction from demonstrations

In domains where robots carry out human’s tasks, the ability to learn new behaviors easily and quickly plays an important role. Two major challenges with Learning from Demonstration (LfD) are to identify what information in a demonstrated behavior requires attention by the robot, and to generalize the learned behavior such that the robot is able to perform the same behavior in novel situations. The main goal of this paper is to incorporate Ant Colony Optimization (ACO) algorithms into LfD in an approach that focuses on understanding tutor’s intentions and learning conditions to exhibit a behavior. The proposedmethod combines ACO algorithmswith semantic networks and spreading activationmechanism to reason and generalize the knowledge obtained through demonstrations. The approach also provides structures for behavior reproduction under new circumstances. Finally, applicability of the system in an object shape classification scenario is evaluated. © 2014 Elsevier B.V. All rights reserved.