Applying Ant Colony Optimization Algorithms for High-Level Behavior Learning and Reproduction from Demonstrations
2015 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 0921-8830, Vol. 65, 24-39 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 65, 24-39 p.
Learning from Demonstration, Semantic Networks, Ant Colony Optimization, High-Level Behavior Learning
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:umu:diva-87257DOI: 10.1016/j.robot.2014.12.001ISI: 000349724400003OAI: oai:DiVA.org:umu-87257DiVA: diva2:708069
FunderEU, FP7, Seventh Framework Programme, 238486