Towards Efficient Knowledge Reuse for Open-ended Learning in Real Robots through Motivation

The current work is focused on providing robotic systems with mechanisms that support lifelong open-ended learning, with the aim of increasing their real autonomy level. In this general scope, robots must discover their goals and learn the skills to achieve them in a priori unknown domains and tasks. Moreover, they must do it in way that allows this knowledge to be reused later to face new situations properly. To advance in this challenging field, this paper describes a specific motivation-based knowledge reuse strategy, together with the contextual processing carried out, within the e-MDB cognitive architecture. This strategy supports the reuse and adaptation of knowledge acquired in previously seen domains to new ones. It has been validated in a real-world experiment with the Baxter robot, which is analyzed and discussed here, that addresses open-ended interaction in a sequence of domains related to object manipulation.