An experience-based approach for cognitive service robot system

In this paper, an FIM (Fitness to Ideal Model) and a DLen (Description Length) based evaluation approach has been developed to measure the benefit of learning from experience to improve the robustness of the robot´s behavior. The experience based mobile artificial cognitive system architecture is briefly described and adopted by a PR2 service robot within the EU-FP7 funded project RACE. The robot conducts typical tasks of a waiter. Temporal and lasting obstacles and standard table items, as shown in the demonstrations of “Deal-with-obstacles” and “Clear-table-intelligently”, are being adopted in this work to test the proposed evaluation metrics, validate it on a real PR2 robot system and present the evaluation results. The relationship between the FIM and DLen has been validated. This work proposes an effective approach to evaluate a cognitive service robot system which enhances its performance by learning.