Extracting kinematic background knowledge from interactions using task-sensitive relational learning

Abstract

To successfully manipulate novel objects, robots must first acquire information about the objects' kinematic structure. We present a method for learning relational kinematic background knowledge from exploratory interactions with the world. As the robot gathers experience, this background knowledge enables the acquisition of kinematic world models with increasing efficiency. Learning such background knowledge, however, proves difficult, especially in complex, feature-rich domains. We present a novel, task-sensitive relational rule learner and demonstrate that it is able to learn accurate kinematic background knowledge in domains where other approaches fail. The resulting background knowledge is more compact and generalizes better than that obtained with existing approaches.