Applying Ant Colony Optimization algorithms for high-level behavior learning and reproduction from demonstrations
Language
EN
Article de revue
This item was published in
Robotics and Autonomous Systems. 2015-03, vol. 65, p. 24-39
English Abstract
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 ...Read more >
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 proposed method combines ACO algorithms with semantic networks and spreading activation mechanism 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.Read less <
English Keywords
Semantic networks
Learning from Demonstration
High-level behavior learning
Ant Colony Optimization
European Project
INTeractive RObotics Research Network
Collections