Coupled Learning of Action Parameters and Forward Models for Manipulation

The effectiveness of robot interaction depends on the robot's ability to perform task-relevant actions and on the degree to which it is able to predict the outcomes of these actions. In this paper we argue that the two learning problems learning actions and learning forward models - must be tightly coupled for each of them to be successful. We present an approach that is able to learn a set of continuous action parameters and relational forward models from the robot's own experience. We formalize our approach as simultaneously clustering experiences in a continuous and a relational representation. Our experiments in a simulated manipulation experiment show that this form of coupled subsymbolic and symbolic learning is required for the robot to acquire task relevant action capabilities.